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Machine Learning in Context, or Learning from LANDR: Artificial Intelligence and the Platformization of Music Mastering


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This article proposes a contextualist approach to machine learning and aesthetics, using LANDR, an online platform that offers automated music mastering and that trumpets its use of supervised machine learning, branded as artificial intelligence (AI). Increasingly, machine learning will become an integral part of the processing of sounds and images, shaping the way our culture sounds, looks, and feels. Yet we cannot know exactly how much of a role or what role machine learning plays in LANDR. To parochialize the machine learning part of what LANDR does, this study spirals in from bigger contexts to smaller ones: LANDR’s place between the new media industry and the mastering industry; the music scene in their home city, Montreal, Quebec; LANDR use by DIY musicians and independent engineers; and, finally, the LANDR interface and the sound it produces in use. While LANDR claims to automate the work of mastering engineers, it appears to expand and morph the definition of mastering itself: it devalues people’s aesthetic labor as it establishes higher standards for recordings online. And unlike many other new media firms, LANDR’s connection to its local music scene has been essential to its development, growth, and authority, even as they have since moved on from that scene, and even as the relationship was never fully reciprocal.
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DOI: 10.1177/2056305119847525
“Create, we’ll do the rest,” insists the tagline for LANDR, an
online music mastering service (About LANDR, n.d.).
Echoing Kodak’s 1888 slogan, “You press the button, we do
the rest,” LANDR promises its customers ease, seamlessness,
and simplicity for the final stages of recording and releasing
music: mastering and distribution. With a single click (and a
credit card transaction), LANDR users can distribute finished
tracks across major music platforms like Spotify, Apple Music,
Google Play, Tidal, Deezer, “and everywhere else that mat-
ters” (About LANDR, n.d.). But this option is offered by many
services on the Internet. The more unique service LANDR
offers is automated music mastering, built on top of supervised
machine learning (ML), branded as artificial intelligence (AI).
Their stated goal is to use ML to automate the kinds of deci-
sions usually made by human mastering engineers. This sim-
ple claim hides as much as it reveals: the term “artificial
intelligence” has become a marketing buzzword in recent
years, papering over many different kinds of ML that are in
use (see MacKenzie, 2017, p. 5). Moreover, it blurs the bound-
ary between a business or operation that might use some kind
of ML for part of what it does, versus an enterprise that con-
sists entirely (or mostly) of ML. For the purposes of this essay,
we consider LANDR’s claims to be an AI-based firm as
socially or culturally relevant, while not assuming that their
primary work involves ML, or that ML is the only important
application of AI.
LANDR’s promise to replace human mastering engineers
also begs the question of what they are actually automating,
or if automation is even the right word for what they do,
since firms and technological systems often redefine the
human tasks they claim to replace. As of now, LANDR’s
core service is a kind of signal processing. Every image that
appears on a screen and every sound that comes out of
847525SMSXXX10.1177/2056305119847525Social Media <span class="symbol" cstyle="Mathematical">+</span> SocietySterne and Razlogova
1McGill University, Canada
2Concordia University, Canada
Corresponding Author:
Jonathan Sterne, Department of Art History and Communication Studies,
McGill University, 853 Sherbrooke St., Montreal, QC H3A 0G5, Canada.
Machine Learning in Context, or
Learning from LANDR: Artificial
Intelligence and the Platformization
of Music Mastering
Jonathan Sterne1 and Elena Razlogova2
This article proposes a contextualist approach to machine learning and aesthetics, using LANDR, an online platform that
offers automated music mastering and that trumpets its use of supervised machine learning, branded as artificial intelligence
(AI). Increasingly, machine learning will become an integral part of the processing of sounds and images, shaping the way
our culture sounds, looks, and feels. Yet we cannot know exactly how much of a role or what role machine learning plays
in LANDR. To parochialize the machine learning part of what LANDR does, this study spirals in from bigger contexts to
smaller ones: LANDR’s place between the new media industry and the mastering industry; the music scene in their home
city, Montreal, Quebec; LANDR use by DIY musicians and independent engineers; and, finally, the LANDR interface and
the sound it produces in use. While LANDR claims to automate the work of mastering engineers, it appears to expand and
morph the definition of mastering itself: it devalues people’s aesthetic labor as it establishes higher standards for recordings
online. And unlike many other new media firms, LANDR’s connection to its local music scene has been essential to its
development, growth, and authority, even as they have since moved on from that scene, and even as the relationship was
never fully reciprocal.
artificial intelligence, machine learning, music, labor, art and aesthetics, scenes
2 Social Media + Society
speakers is manipulated to look or sound a particular way.
This work of manipulation (or modulation) is called signal
processing (Sterne & Rodgers, 2011), and mastering engi-
neers are the group of people who apply a final round of
signal processing to audio before it is made public through a
formal release (such as an album, a film or a TV show), or
simply through uploading to a website or streaming service.
Mastering engineers are the last line of ears before sound
comes out of speakers. For our academic readers, mastering
might best be understood as the audio equivalent of typeset-
ting and the creation of page proofs for a publication. As
Mandy Parnell (2017), an engineer who has mastered record-
ings for artists like Björk, Feist, The XX, and Tim Hecker
explains, “we need to make it fit inside the world. How is it
going to sound on the radio or on a playlist?”.
To do this for music, a mastering engineer usually receives
a stereo recording (or in the case of game or film audio, a
multichannel mix) and then adjusts the relative loudness of
different frequencies (equalization, or EQ), the stereo bal-
ance, and the relative loudness of different parts of the music
(dynamic range). They may make other adjustments as well.
Mastering studios are usually more carefully acoustically
tuned than recording studios—especially in the range of bass
frequencies. Because bass frequencies are harder to repro-
duce and hear, this marks one audible aesthetic difference
between mastering and other kinds of audio work, a differ-
ence we will explore later in the article. Mastering engineers
are often (though not always) specialists, doing only master-
ing day after day. And though mastering engineers may use
the commercial software widely available to musicians, they
also often have expensive, highly customized, or customiz-
able audio processors as part of their studio setup.
Like any other technology, AI exists within webs and
flows of culture and power. Recent scholarship on AI has
focused on its implications for labor, privacy, bias, and gov-
ernance (Campolo, Sanfilippo, Whittaker, & Crawford,
2017). But increasingly, ML will become an integral part of
the processing of sounds and images, shaping the way our
culture sounds, looks, and feels (see also Manovich, 2018).
LANDR represents an early example of a self-described ML
application in the domain of media aesthetics, and they are
one of a group of businesses using ML for audio signal pro-
cessing. For example, the software company Izotope uses
ML to design processing routines for software that resides on
an end user’s computer. Their products include audio master-
ing software, forensic applications, and applications for mix-
ing vocals and music. CloudBounce offers a mastering
service similar to LANDR’s, but it leaves more to the end
user in terms of decision-making and therefore requires a
more practiced end user. There is also a long history of auto-
matic mastering applications in hardware and software (a
recent, free to try example is Curioza’s Auto Audio Mastering
System that works by comparison), some of which allow the
user to input a “reference track,” which provides a set of
sonic goals for the processing of a new track. We chose to
study LANDR in depth over these others in part because it
falls in a sweet spot of delegation and opportunity. Compared
to CloudBounce and Izotope, LANDR makes more choices
for its end users, thus making the strongest claim toward aes-
thetic automation. We were also particularly well-positioned
to study LANDR: we are both situated in Montreal and could
visit its offices, talk with its current and former employees
(some of whom we knew socially), and treat it as an institu-
tion “on the ground,” as well as an interface and a platform.
It has existed in our social world for years, and Jonathan first
met employees of LANDR before it was even a mastering
company. A brief comparison to Izotope’s approach appears
below, and we offer a fuller institutional history of LANDR,
as well as its place in the history of audio mastering in a
companion piece (Sterne & Razlogova, forthcoming). For
now, it is enough to know that LANDR is not the first or only
attempt to automate mastering, and that mastering itself has
a dynamic and varied history. It is not just one thing.
Kate Crawford (2016) has argued that to understand algo-
rithmic systems we need to “broaden our scope to include the
array of human and algorithmic actors developing a space—
sometimes in collaboration, sometimes seeking to counter
and outwit each other” (p. 81). We understand LANDR, mas-
tering, and AI more broadly, as sets of tools, protocols, and
practices that operate in the world and that are shaped by
people. This is especially important when studying corporate
applications. We cannot know how LANDR actually works,
or where ML happens in the process. While it could be fully
automated, comparing a recording to a massive dataset and
then reconstructing it to match the dataset entirely through
machine learning-based processes, this is unlikely, as the
research literature on automated audio mastering shows no
progress in this area, and we can find no evidence of patent
filings by LANDR or anyone else for an entirely AI-based
approach to mastering. More likely, it uses ML for part of the
process, for instance in analyzing the sound of an uploaded
audio track, and then selecting from a matrix of preset pos-
sibilities for processing. We will discuss this ambiguity fur-
ther below, but it is actually a constitutive feature of studying
software “in the wild” (Seaver, 2017). LANDR’s opacity
results from a combination of “intentional secrecy, technical
illiteracy, and the sheer scale and functional protocols of
machine learning” (Burrell, 2016, pp. 4–5; see also Pasquale,
2015). Rather than understanding the secrecy around its
algorithms as a block to our study (though of course, we
would love to know everything about how it works), we
instead treat LANDR’s obfuscation of its own internal work-
ings as a constitutive feature of its social and cultural exis-
tence. We also do not assume that its use of ML is a radical
break from other signal processing techniques.
Thus, in the spirit of Crawford, Seaver, and Burrell, we
offer an analysis of LANDR through a series of nested con-
texts and fields: industries, scenes, users, interface, signal
processing, and sound. We spiral in from bigger contexts to
smaller ones to parochialize the ML part of what LANDR
Sterne and Razlogova 3
does. LANDR’s operations at each level are all related but
not in a necessary or predictable way. Nor does one con-
text—for instance, the AI aspect of what they do—auto-
matically determine how they act on other levels. To show
that the “effects” of AI are heavily shaped by the social
fields and contexts in which it is allowed to operate, we
examine several agonistic contexts for LANDR: their place
between the new media industry and the mastering indus-
try; the music scene in their home city, Montreal, Quebec;
DIY users and independent engineers; and, finally, just one
user’s experience of the LANDR interface and the sound it
produces. Throughout, we emphasize how the technology
reveals itself in points of dissonance or breakdown (Ahmed,
2006, pp. 46, 48). To this end, we interviewed users and the
co-founder of LANDR. We conducted additional ethno-
graphic discussions with musicians and mastering engi-
neers and participated in related public events at LANDR
headquarters and local film festivals. We studied the inter-
face and the company’s public speech as discourse. We
studied LANDR’s impact on the local independent music
scene. Elena, a show host at McGill station CKUT since
2013, featured and interviewed dozens of local bands on
the air, and interviewed the same musicians before and after
LANDR came on the scene. Ours is the second published
media study of LANDR. Thomas Birtchnell’s study (2018;
it appeared as ours was under review) is built around studio
site visits in Australia and interviews with audio engineers.
It provides valuable ethnographic evidence of how engi-
neers understand mastering, and how they view the poten-
tial threat of automation of mastering work. Our study lends
additional support to his claim that although LANDR sug-
gests itself as an alternative to working with a mastering
engineer, it actually has not eliminated jobs, instead leading
to a reassessment of what mastering “is.” This suggests that
the claim that AI will eliminate jobs because of its techno-
logical dimensions is at best incomplete: context matters.
Elsewhere, our approaches diverge because of geography:
we were able to subject LANDR itself to ethnographic
scrutiny by visiting the office and talking with current and
former employees (only some of whom we can directly
quote in this article). We talked with musicians, as well as
recording engineers and industry experts (including one
notable public critic of LANDR). We are considerably
more agnostic about the role ML plays in LANDR’s soft-
ware than Birtchnell (2018, p. 2). We also found that some
of the most important “effects” of LANDR come not from
its approach to algorithms or ML, but its status as a venture
capital-funded corporation, one working on a Silicon Valley
inspired model of the so-called industrial disruption. Thus,
the politics of AI cannot be separated from the politics of
corporate capitalism, regulation, and resistance.
The period of working on this article coincided with
Jonathan mixing three records (he has played, recorded, and
mixed music since the late 1980s). He took advantage of this
opportunity to do some comparative analysis. He ran all of
the mixes through LANDR, as well as taking two finished
sets of mixes to mastering engineers. LANDR’s sales pitch is
that what they do is equivalent to what a human mastering
engineer does. As we will show, this claim depends entirely
on what you mean by “mastering.” LANDR’s success is
defined in part by limiting the problems it is trying to solve
while finding ways to market new uses for its products. All of
Jonathan’s mixes featured musicians playing original music
on “standard” rock and jazz instruments (guitars, keyboards,
synths, drums, winds, vocals, and computer) with some signal
processing done during the recorded performance and some
done afterwards in mixing. For an instrumental post-rock
record (Volte, “Selfie, gluten, et lâcher-Prise”), he hired
Harris Newman at Grey Market Mastering in Montreal.
Newman helped define the Montreal post-rock sound, is a
well-respected independent mastering engineer, and is a part
of the Montreal scene. Jonathan and Newman have an ongo-
ing professional relationship, since they have worked together
before. For a vocal- and lyric-driven Canadiana record (Hard
Red Spring, “Summerpool”),1 Jonathan hired Freddy Knop of
Listeners Mastering in Berlin, whom he found through an
Internet search. Knop is an independent mastering engineer
and also co-founded HEDD, a high-end speaker company.
Both mastering engineers work alone and are at the middle
level of the industry. They are not the high-end mastering
houses that define the sound of major label releases, like
Sterling Sound, Gateway Mastering, Abbey Road, or
Masterdisk, and their prices are more affordable for indepen-
dent musicians and smaller labels. At the same time, they are
both successful professionals in the field and have established
reputations in their niches. As part of the research for this
article, we also interviewed Larry Crane, editor of Tape Op,
the world’s largest recording magazine, and a working engi-
neer and producer. Crane has a synoptic view of the industry
and has been a vocal critic of LANDR. Yet he also pointed out
that the differences between the kinds of mastering engineers
Jonathan used for the project (who charge between US$75
and US$150 per hour or per track) and US$5–US$6,000 per
record mastering houses at the top of the business are minis-
cule at best: in fact, he advises his own clients to avoid these
studios (L. Crane, Interview by Jonathan Sterne, July 28,
2018). While mastering engineers at the top studios might
have more expensive equipment and more experience, their
work process is not fundamentally different from that of the
two engineers we studied for this article. A top-level master-
ing house might have more elaborate sound treatment, or a
wider range of high-end audio processors to choose from, but
Newman and Knop were already working with high preci-
sion, highly specialized equipment. At the top level of the
industry, artists and labels are paying for a curriculum vitae as
much as a skill set: to be mastered by the same engineer who
did other famous recordings. Newman, meanwhile, spoke of
keeping his prices affordable to serve a certain stratum of
musicians whose music he values. As of the time of submis-
sion of this article, LANDR offered plans between US$4 and
4 Social Media + Society
US$25 per month for unlimited mastering of audio tracks (the
cost difference has to do with the audio definition of the final
product—the lowest usable plan is probably US$9 a month),
as well as options for high-definition mastering of individual
tracks. This makes them much less expensive for individuals
and also places them in a different kind of economy, as we
will discuss throughout this piece.
Both Newman and Knop are deeply connected to their
own local music scenes, and neither is short of work. LANDR
was also a part of the Montreal music scene at first but in a
very different way. It collaborated with local institutions,
employed local artists, and used their tracks and experience to
perfect its mastering algorithm. While initially this relation-
ship was a symbiosis of sorts, it became more problematic as
LANDR reinvented itself as a platform, expanding into other
services such as digital distribution and promotion. Yet its
connection to a scene was also essential for LANDR’s claims
about why musicians should trust it. We document how the
company operated within these and other social relationships.
LANDR, therefore, offers an early test case for AI’s relation-
ships to other kinds of media industries and practices. LANDR
also offers an early test case for arguments about AI and labor,
showing its effects on the labor force can be uneven and con-
tradictory, shaped by the specific contours and limits of the
industry rather than the “impact” of AI itself (Levy, 2015).
While Newman may have helped to define the sound of a
genre, his practice in itself does not require him to try to
define or limit ideas of what other mastering engineers can
do. LANDR’s approach, meanwhile, means that it aims to
stabilize the referents of the term mastering for its purposes
(MacKenzie, 2017, p. 212). It extends some of the methods
of classification of sound media aesthetics first developed
for music recommendation and recognition engines (Freire,
2008; Seaver, 2013; Razlogova, 2013, 2018), building out a
kind of auditory media standard. In other words, if you
accept LANDR’s definition of mastering, then it can master
music. If you do not, then it cannot. This is at the heart of
many controversies around AI replacing human labor, and
something with which philosophers and science fiction writ-
ers have struggled for decades (e.g., Bolter, 1984; Dreyfus,
1972). For the purposes of this article, we strategically accept
LANDR’s invitation to compare it to mastering engineers,
but we do so to highlight this process of redefinition of an
activity for the purposes of making an AI-based practice
socially commensurate with it. For any ML to be successful
at a given cultural task, the people behind it need to circum-
scribe the terms on which it can be successful: a “finished”
recording is an aesthetic judgment and a moving target. In
other words, the entrance of AI into a cultural field marks a
moment of social and definitional contest.
Mastering Houses as Firms: LANDR
Versus Independent Engineers
Grey Market’s, Listeners’, and LANDR’s physical layouts as
businesses can reveal aspects of their operating logics and
media practice (Martin, 2003, p. 9). The mastering studios
visited for this study (and they are typical in this respect)
look like professional recording studios in miniature. There
is no space for performance or tracking, and a large multi-
channel mixing board is not necessary. They are more like
workshops for crafting sound, designed for careful compari-
son and auditioning and for independent work. They high-
light mastering as an artisanal practice, undertaken by a
skilled professional, who works at one project at a time.
Grey Market is located in a post-industrial building, off to
the side of a major local analog recording studio, Hotel2Tango.
As depicted in Figure 1, the studio is a single room, acoustically
treated for maximum clarity. Newman works at a desk full of
equipment: some is for signal processing and some is for com-
paring the processed and unprocessed sound. Freddy Knop’s
studio (Figure 2) is more makeshift but follows the same pat-
tern. A smaller room than Newman’s, in an old Berlin apart-
ment, Listeners Mastering also uses high-end speakers, acoustic
treatment, and a few small racks of specialized mastering gear.
In contrast, LANDR’s corporate headquarters has all the
trappings of a new media company. LANDR presents itself as
both a music business and a new media business, but the
emphasis is on the latter. Set up in a post-industrial space, in a
building that is turning over from artist lofts to businesses, the
main corporate office has the usual islands of cubicles and
desks in the middle of the space, surrounded by a ring of acous-
tically isolated meeting rooms, as shown in Figure 3. Like other
music tech companies, LANDR has dedicated, acoustically
isolated rooms for listening to and manipulating audio and a
small performance space, where musicians can play. The space
is adorned with the usual new media business comforts: a pin-
ball machine, good coffee, and comfortable seating that show
the Silicon Valley-style disdain for “traditional” office culture
(see Ross, 2003; Saval, 2014, pp. 259–277; Turner, 2009).
Even from the hallway outside the business, you would never
know LANDR is an audio mastering company. Although all
Figure 1. The view from Newman’s mixing desk at Grey Market
Mastering. One can see the specialized equipment, high-end full
range speakers, and acoustic treatment behind the speakers.
Note that the darkness is an artifact of the photograph—the
room itself is sunny and well-lit. Photo by (and courtesy of)
Harris Newman, used with permission.
Sterne and Razlogova 5
three firms have repurposed space—Grey Market is also in a
post-industrial space, and Listeners is in an apartment—
LANDR’s layout reflects an ideal of organizational flexibility
that is part of the turn toward open plans among corporations
since World War II. While Grey Market and Listeners both take
advantage of the flexibility of architectures, their layouts repre-
sent clear commitments to a single organizational model: the
mastering engineer in a workshop. In contrast, LANDR’s lay-
out embodies a culture that anticipates and thrives upon its own
volatility (Thomas, in press, p. 31).
As a new media business, LANDR is aggressively com-
mitted to promotion, and partakes of some of the “disrup-
tion” talk popularized by companies like Uber and AirBNB
(Graham & Shaw, 2017). This is a major departure from tra-
ditional mastering firms. As a business, mastering is like a
throwback to the professional and promotional practices of
an earlier era in the history of music production. Whereas
self-promotion and a legible and ongoing relationship with
their audiences have become essential for working musicians
(Baym, 2018), mastering engineers still largely work by
word of mouth and reputation. It is true that one can find
advertisements for new mastering engineers online and in
magazines like Tape Op, and that mastering studios’ websites
are search engine optimized. But promotion for mastering
engineers is a matter of some social subtlety, tied to the pro-
fession’s self-conception. Few mastering engineers maintain
an aggressive social media presence as mastering engineers.
Success means not having to promote yourself too much or
too loudly. Indeed, the latter actions can be read by other
people in the business as a sign of not being good enough at
what you do. In this way, the promotional culture of master-
ing engineers mirrors that of artists before the age of social
media: when a certain level of mystery worked as well in
some cases as familiarity and transparency.
LANDR’s marketing rhetoric echoes the claims to ease,
seamlessness, and creative flow that most media compa-
nies now pitch to their users—even if that is not the actual
goal of most interface designs (Coyne, Parker, & Rebelo,
2004; Manovich, 2013, p. 100; Kember and Zylinska 2015,
p. 18; Simon, 2018). By new media business standards,
they are following established business practices. By more
established mastering industry standards, their approach
might be considered both uncouth and a direct threat.
LANDR’s website is considerably more elaborate and
detailed than the sites of traditional mastering houses.
LANDR has active feeds on Facebook, Instagram, and
Twitter. They buy ads on Facebook and Google—and both
authors have seen ads for LANDR pop up on our screens
(though our participation in this project could also have
something to do with that). Over their existence, they have
promoted themselves aggressively through press releases,
participation on industry panels, tech business pitch-offs,
and partnerships with other industry players. In our inter-
view with Justin Evans, he remarked on Larry Crane call-
ing LANDR “the devil.” When asked about it, Crane did
not recall the exact context, but he objected to LANDR’s
business model more than anything else: “it doesn’t come
from a place of making art,” he said, referring to their
approach to marketing and their reliance on venture capi-
tal. Even if it could be argued that LANDR is more honest
about their promotional strategies, where mastering engi-
neers are traditionally more discreet, LANDR’s brash pro-
motional campaigns clearly signal that it is more like a
new media company and less like a mastering house.
But there is more to it than that. LANDR is not like a
mastering house that needs to bring in enough income to
cover operating costs and feed an engineer or two. LANDR
has investors who have cumulatively poured CAN$10.4 mil-
lion into the company (LANDR). With investors come
benchmarks—the need to reach a particular size and income
level at a particular time—and all of the requirements that
come with taking in venture capital, irrespective of the kind
of business under consideration. Few successful mastering
houses aim to diversify the portfolio of services they offer
Figure 2. Freddy Knop in motion at Listeners Mastering in
Berlin. Again, the specialized equipment and acoustic treatment
are visible. Photo courtesy of Knop and used with permission.
Figure 3. LANDR executive Pascal Pilon in the corporate office.
We were not able to take photos on our visit and instead had to
rely on the readily available photos from the web.
million-1.4054430 (last updated April 2017).
6 Social Media + Society
clients apart from those specific to mastering. In fact, it could
be argued that the most successful mastering studios are the
most specialized: they just do mastering. Knop is an interest-
ing exception here that proves the rule. He only derives part
of his income from mastering and the rest from his work at a
speaker company. Though obviously there is some relation-
ship in terms of involvement with audio, he presents these as
two entirely separate activities, and does not appear to use
his work at HEDD to promote his mastering (apart from a
mention on his website), or vice versa.
LANDR, meanwhile, aims to diversify its services.
During the period of our study, they moved into music distri-
bution; they created mechanisms for sharing and comment-
ing on tracks during the mastering process; they created an
instant “release” button; they released a set of sample packs
for people to use in their music; they published advice col-
umns for musicians and engineers; they held parties and edu-
cational events in their Montreal headquarters; and they have
begun a music promotion service, cutting into the work of
“dream merchants” like Taxi and other music publishers.
This is not the strategy of a mastering engineer looking to
expand their business. This is a classic platformization strat-
egy, and is not unique to ML or AI in any way. LANDR
defines itself as “the creative platform for musicians,” and
the differences between a platform and a mastering house are
legion. Platforms are defined by semantic ambiguity.
Platformization allows companies to represent themselves
differently to different audiences (Gillespie, 2010, p. 359). It
also allows for ambiguity of business model, a fluidity of
function, and a much more variable set of potential relations
to its user base than mastering engineers or mastering houses
would have with their clients (Van Dijck, 2013, pp. 89–109).
Most of LANDR’s business strategies involve trying to get
its users to engage in more ways, more often, and more fully.
And although LANDR does not own a musician’s tracks,
their user agreement appears designed to give them the rights
to the data and metadata derived from the analysis and pro-
cessing of those tracks. Though this fits a new media busi-
ness model well, this is not traditionally how mastering
businesses develop, whether they ultimately succeed or fail.
Local Context: LANDR’s Shifting
Relationship to the Montreal Music
LANDR built up its initial reputation and data bank by relying
on its connection with the Montreal music scene. The city has
produced a disproportionate share of award-winning Canadian
artists. Montreal hosts a large number of self-employed young
musicians and promoters (DIY2), as well as a dozen festivals,
such as MUTEK and POP Montreal, several freeform AM–
FM and online radio stations, and a slew of underground per-
formance spaces (Campbell, 2013; Straw, 2014). National and
provincial music grants, such as FACTOR, combined with
still relatively low rents, make it possible to produce records
and organize events in the city with less cash than in most
North American cities of the same size (Piper, 2014). On aver-
age, Montrealers go to more live concerts than Canadians in
Toronto and other cities. With tickets as cheap as CAN$10 or
pay-what-you-can, touring musicians report encountering less
profit but a more enthusiastic audience in Montreal than else-
where in Canada (A. Lumley, Interview by Elena Razlogova,
May 20, 2013).
Although LANDR has aimed at business growth and prof-
its as much as any Silicon Valley digital startup, it has bene-
fited from Montreal’s low-cash music economy. The music
industry context mattered especially because LANDR,
founded in 2014, predated the emergence of Montreal as an AI
hub by 2016, when researchers Jean-François Gagné and
Yoshua Bengio founded Element AI, the largest private AI lab
in Canada, and especially in 2017, when Microsoft purchased
local company Maluuba, reinvented as the Microsoft Research
lab (Stark & Pylyshyn, 2018). When we first visited LANDR,
many employees, including Evans, played in local bands or
had other music-related projects. LANDR’s employees have
included Public Relations and Artist Relations Manager Tasha
Anestopoulos, then a DJ and radio host at McGill’s station
CKUT, and event curator and blogger Laetiticia Trandafir, aka
electronic musician Softcoresoft. Both already had long-
standing connections allowing them to organize special events
and online features with Montreal musicians and producers.
For several years, LANDR partnered with electronic music
festival MUTEK, curating special programs at the festival. In
turn, local programmers appeared at public panel discussions
organized at LANDR headquarters.
Software companies tend to describe themselves as soft-
ware companies that provide a certain kind of service. Uber
and AirBNB do not want to be thought of as being in the
taxi or hotel business. Conversely, LANDR was somewhat
unusual in trying to pitch itself as part of a local music
scene. In 2016 and 2017, LANDR hosted a series of events
featuring local electronic artists and music promoters. In
itself, this was somewhat unusual. Elena attended one such
event, on “music curation,” in August 2016, featuring Patti
Schmidt of MUTEK, Anthony Galati of Never Apart gal-
lery, Dan Seligman of POP Montreal Festival, and Sarah
Lamb of Hushlamb electronic music collective. This small
informal gathering included (mostly Anglophone) local
musicians, promoters, radio hosts, and fans. Many in the
audience knew each other, the speakers, and the projects
they represented. Newcomers, including an incoming
McGill student and new CKUT volunteer, seemed comfort-
able participating. The in-depth discussion included
informed questions from the audience, and focused, among
other things, on strategies of organizing events responsive
to local racial, gender, and language politics, and on pro-
ducing innovative shows on a limited budget. Beer was
served. Elena left feeling that the LANDR offices func-
tioned as a hub that made new practical solutions and new
collaborations possible. The gathering included no formal
Sterne and Razlogova 7
publicity for LANDR, but the atmosphere of an activist
block party certainly contributed to its credibility with local
musicians. All of these events evidence LANDR’s con-
certed effort to act as a hub for the Montreal music scene.
Yet this should not be taken as evidence of success: in our
own travels through various iterations of the city’s music
scenes, LANDR was present as a force and an employer,
but nobody we spoke with outside LANDR itself presented
it as any kind of cultural hub.
LANDR’s publicity and platform expansion strategies
draw upon local wisdom as well. Its slick booklet The
No-Bullshit Musician’s Guide to DIY Self-Promotion (2016),
ghostwritten by a local hip-hop musician, delivers a slice of
advice Elena heard directly from local artists and label repre-
sentatives at “Lil’ Biz” seminars organized for novice musi-
cians by POP Montreal several times a year (J. Sadler,
Interview by Elena Razlogova, November 29, 2018). Among
other things, The Guide advises readers to use SoundCloud
and distribution services TuneCore and CD Baby. LANDR
had partnered with these three platforms early on because
independent artists use them to disseminate their music. When
in 2017, LANDR offered its own distribution services, the
move was a well-informed practical step in its evolution.
LANDR subscribers can now choose to distribute their tracks
for free to all major streaming services. Clients can also forego
mastering services and pay for a distribution-only option.
At the same time, LANDR presumes certain kinds of DIY
practices to take advantage of them and leaves out the rest.
For example, The Guide omits some DIY issues featured
prominently in POP Montreal workshops, such as taxes for
self-employed artists and grant applications, which confront
the harsh economics of trying to make it as a DIY musician.
Although The Guide advises musicians to use Bandcamp,
LANDR did not partner with Bandcamp. When asked why,
Evans explained that Bandcamp artists already use LANDR
(J. Evans, Interview by authors, August 24, 2016). This is not
exactly the case: as of August 2018, out of hundreds of
Montreal artists on Bandcamp, only 39 have listed mastering
with LANDR. A more likely explanation may be that
TuneCore and other LANDR partners are better integrated in
digital licensing and large-scale online markets than
Bandcamp, making cross-promotion and bundling of services
more profitable. TuneCore not only sells digital distribution
to all major streaming services but also offers marketing help,
detailed financial reports, and placement of tracks on Spotify
playlists. Conversely, Bandcamp does not offer distribution to
streaming services: it caters to musicians who value auton-
omy album-like formats for digital releases. Having to please
its investors, LANDR orients its relationships with distribu-
tion platforms to fit its financial growth strategy.
LANDR has benefited from state support for music in the
city and a larger, eager, and relatively cheap labor pool. Their
employees from the local music scene focused on their own
creative projects on the side and happily filled short-term pub-
lic relations and artist relations positions, following a standard
digital gig economy model. As a result, LANDR saved on
salaries, attracted new clients through these employees’ con-
nections, and used the local music tracks to perfect its algo-
rithm in genres that could help the company expand into
markets elsewhere. LANDR’s PR wing clearly believed that
this approach was important for establishing credibility and
trust. In our interview with Evans, he stressed their desire to be
understood as part of the music industry. Evans also reported
working with local EDM (Electronic Dance Music) and hip-
hop musicians to perfect the algorithm’s ability to master
EDM and hip-hop music (J. Evans, Interview by authors,
August 24, 2016). Drawing on local DIY practices has helped
LANDR to keep hold on its original base of beginner and ama-
teur musicians and build its credibility with users, even as it
expands into mastering for TV, advertising, film, and other
highly capitalized fields.
However, this connection to the local community appears to
have been only a temporary step in the company’s growth, and
it has led to rifts, as one local mastering engineer’s story shows.
While developing his expertise at mastering EDM, he held
down a good-paying job at a local college. Recruited by LANDR
to help them with their EDM mastering, he resigned from his
teaching job, ostensibly for LANDR’s better salary and benefits.
But after he had trained the software to better work with EDM
tracks, LANDR laid him off. He is now running an independent
mixing, mastering, and audio assistance service in Montreal
(Anonymous, Interview by authors, September 6, 2018). While
this appears to be a classic example of AI replacing laborers, the
truth shows a more mixed story: this engineer now runs a thriv-
ing business, has plenty of clients, and a growing local reputa-
tion in the electronic music scene, even being brought in as an
expert for festivals like MUTEK. At the same time, we should
not romanticize the outcome: LANDR did not treat him well.
But while it may have replaced him inside the company, it did
not, in the end, eliminate the space for his work as a mastering
engineer. In fact, his own testimony about LANDR shows those
contradictions: while he is clearly angry with the company for
how he was treated, he also acknowledges that their software
can work reasonably well. At the same time, he has also had
clients who sought him out because they were not satisfied with
LANDR’s masters. This is exactly the kind of agonistic scenario
outlined by Crawford: LANDR at once offers corporate employ-
ment to a mastering engineer who otherwise would not have it
and takes it away; LANDR promotes its own mastering service
as an alternative to mastering engineers, and at the same time
users’ dissatisfaction with LANDR can lead to more business
for local mastering engineers.
As of 2018, LANDR had stopped hosting intimate public
events at its offices (it continues to coproduce music shows
with galleries and festivals in Montreal). Evans left the com-
pany. LANDR has also set up offices in Los Angeles and
Berlin. Montreal may be becoming less and less relevant as
the company courts Hollywood investors and clients, and
electronic music labels in Europe. As start-up founders pass
the reins to financiers and management experts, the role of
8 Social Media + Society
practicing musicians and DIY scenes in the creation of the
algorithm is easy to forget.
Who Gets to Master? Under What
Circumstances? LANDR’s Users
LANDR’s echo of the Kodak pitch—“create, we’ll do the
rest”—is not accidental. Eastman Kodak aimed to make pho-
tography an amateur pursuit, freeing the photographer from
the laborious task of reloading film into a camera after each
photo as well as photo development, and touting the prom-
ises of standardization, mass production, and the 19th cen-
tury fantasy of a simple button press leading to something
happening (Marvin, 1988). A hundred thirty years later,
LANDR uses the same language in a different landscape to
appeal to amateur musicians who have taken on board the
idea that they can do everything themselves. Rory Seidel,
Executive Creative Director at LANDR, speaking on a POP
Montreal panel about music and technology on September
28, 2018, likened AI in LANDR to an “autofocus on your
camera,” a simple tool that “applies a custom chain for mas-
tering.” Seidel told the audience that he joined the company
early on as an independent musician looking for “tools to
solve problems that I and my friends had.” The irony is that
LANDR suggests that the musician’s main work is to create.
Yet DIY has been all about role collapse and compounding
responsibilities: a self-managed artist writes, records, mixes,
performs, manages an online social media presence, pro-
motes their work, and handles finances (Bell, 2014). In that
context, LANDR presents itself as a combination of labor
savings and sound improvement.
Musicians may use LANDR for cost savings as a substi-
tute for commercial mastering, as part of a DIY ethos, or as
an alternative to spending nothing at all on mastering. We
surveyed all Bandcamp tracks using LANDR and originating
from Montreal. Bandcamp “is emblematic of the paradig-
matic turn within the music industry triggered by digitiza-
tion” (Kribs, 2017, p. 6): fulfilling some of the functions of a
label, a store, a streaming service, and a band website, it
allows musicians to keep a higher proportion of the money
from sales of their music than iTunes or Spotify, and it is (as
of this writing) more profitable than SoundCloud. We chose
it because it is a preferred platform for independent musi-
cians and bands and also because it has much more robust
facilities for credits. Because its interface takes more from a
traditional “album” model, musicians are more likely to
credit a mastering engineer or service on Bandcamp than on
SoundCloud or Beatport. iTunes and Spotify actively remove
a wide range of liner note information from albums that orig-
inally came with it, anonymizing and minimizing the work
of a host of laborers, engineers included. The tracks we sur-
veyed range from amateur tracks composed, performed,
recorded, and mixed by the person at home (Natation, In
Circles, 2018), to a performance recorded live in a club or a
radio studio (CJLO 1690AM—Baked in the Oven, Vol. 3,
Chrispy Chords, 2015), to a professional album produced in
a recording studio and mixed by an engineer (CO/NTRY,
Africa, What You Doing With The Bottled Water?, 2014). In
the first case especially, LANDR provides artists with confi-
dence because it gives some kind of external confirmation
that their tracks are “mastered” and may also add a pleasing
sonic sheen. The second and third cases show how the choice
of mastering or not, and how, can be used selectively by the
same artist depending on their political purpose, genre aes-
thetics preferences, and the type of funding.
Cost, activist politics, and genre seem to be the main fac-
tors for Heathers. Elena first met the band during their live
performance at CKUT in 2013, as a new post-punk grunge
trio of female friends formed as a Sleater-Kinney cover band
for a Rock Camp for Girls benefit. Their first album, recorded
and mixed by a friend, Dorian Scheidt, in 2014, has no mas-
tering credits. The next three are all mastered by LANDR
and mixed by Patrick McDowall, using the studio at
Concordia University’s radio station CJLO, where he is a
production engineer. McDowall routinely uses LANDR to
master tracks from live sets aired at CJLO. In 2016, a mem-
ber of Heathers confirmed to Elena in conversation that cost
was their main consideration in going with LANDR
(H. Hardie, Personal conversation with Elena Razlogova,
June 18, 2016). By 2018, the band has achieved considerable
recognition in the city and has toured in North America.
They could probably manage to afford a mastering engineer.
But Heathers members are still focused on the local DIY
scene. They continue to play benefit and free shows at under-
ground venues in the city, and for their 2018 album, they
stayed with Patrick McDowall and LANDR.
State funding and genre seem to govern the choices of elec-
troacoustic musician Nick Schofield. Elena first interviewed
Schofield in 2013, when he had already been playing for a few
years, having established connections in the industry as a host
of a popular CKUT radio show Underground Sounds, cover-
ing the local music scene (N. Schofield, Interview by Elena
Razlogova, May 21, 2013). He has participated in several
projects since then. Saxsyndrum, a raucous rock band com-
posed of two to four male performers at different times, used
LANDR once when mastering an unfunded album, also mixed
by McDowall. But the group professionally recorded and mas-
tered all albums funded by FACTOR and/or SOCAN. Rêves
Sonores, released by an activist Howl Arts Collective, a more
personal and experimental electronic interpretation of acoustic
performances, issued one album mastered by a local engineer
who ran a studio out of his apartment, Dimitri Condax, and
another mastered by Schofield himself. Schofield’s latest
project, an electronic duo called Best Fern, released its first
album in 2018 with FACTOR support and used Harris
Newman of Grey Market for mastering. Another, a solo
unfunded 2018 project, Water Sine, composed and performed
with one synthesizer, one effect pedal, and a field recorder,
was mastered by Evan Tighe, a friend and a freelance drum-
mer who also does “boutique” mastering (N. Schofield,
Sterne and Razlogova 9
Interview by Elena Razlogova, November 20, 2018). Here,
LANDR helped musicians to master in between grants but
was abandoned once funding, or friends, became available.
In Montreal, then, using LANDR does not seem to com-
promise one’s status as a serious musician, underground art-
ist, or activist. None of the musicians Elena talked to
considered it illegitimate for other bands with limited bud-
gets to use LANDR. At the same time, all expressed prefer-
ence and admiration for local mastering engineers. Harris
Newman, in particular, has been called an “album therapist,”
who can help an artist to let go of an album in emotional as
well as technical ways (N. Schofield, Interview by Elena
Razlogova, November 20, 2018). For many independent
musicians, using LANDR is simply one choice among many,
depending not just on their financial means but on how they
perceive their craft. It depends on whether they choose to
apply or not for government grants, whether they choose a
career in experimental music (works worse with LANDR) or
grunge rock (works better with LANDR), or whether they
can rely on a discount from a friend to mix and master their
records. Within a universe of contradictory and limited
options, LANDR is sometimes seen as a legitimate, cost-
effective, and—despite its claim to replace mastering engi-
neers—ethical choice for mastering.
At the same time, algorithmic mastering forecloses some
aesthetic developments in DIY music making that interaction
with a live engineer would foreground. Darcy Proper, a
Grammy-winning mastering engineer from Wisseloord
Studios, argues that DIY musicians who mix in “an uncon-
trolled environment” of a home studio may “fix” an emotion-
ally evocative but flawed sound that an experienced
mastering engineer would advise to keep:
I think that’s an important part in the decision-making process.
If you leave those decisions to the people who have been on that
journey the whole time, their tendency might be to fix things
that aren’t broken and thereby take the beauty and the joy out of
the nuances and the beautiful flaws. (Toulson, 2016)
LANDR may smooth over unconventional sounds—it has no
other option than to make normative mastering choices. This
may be the reason why artists like Nick Schofield do not use
LANDR for experimental tracks.
In promoting its version of sound improvement, LANDR
creates new uses for mastering. Consider the experience of
“Luke,”3 an Atlanta-based hip-hop producer interviewed
during this project in 2016. Luke produces sound beds for
rappers to use in their music—he makes beats, which he
then sells. Beat making and production are central to how
hip-hop gets made, which is different from the DIY and
band operations described above. Rappers will often pur-
chase beats to rap over, rather than coming up with the
music themselves or associating with a single beat maker
over a long term. Luke is also an early career musician,
looking to build his business. When asked about his practice
in August 2016, around the time we visited the LANDR
offices, he responded that he had “really gotten into master-
ing.” By this, he meant running his finished beats through
LANDR, to give them more oomph and pop, to help them
stand out in a very competitive environment—he is one of
many producers in the Atlanta scene—where he wanted his
work to stand out and appeal to potential clients. Here,
LANDR appears as a kind of value added, an additional
layer of polish in a competitive environment and it also rep-
resents a kind of definitional shift. In a more traditional
music production context, a recording like Luke’s would not
be mastered until after the rapper’s vocal track had been
recorded and the track fully arranged. Mastering was also
expensive, and not something one would normally do in the
beat-making business. That would be for the client or their
label to take care of. In the “normal” way of doing things,
mastering was not a logical thing for him to do.
This approach also introduces a sonic problem into the
music. If LANDR treats a track as “finished” when it is mas-
tered, adding vocals could lead to level-balancing problems at
the next stage of production. One thing LANDR does is raise
the average volume of a recording. Imagine a cup filled with
water. An unmastered track has enough headroom to add a rap
vocal over it. A mastered track might well go right up to the
rim. But unlike a cup, a digital audio track cannot spill over
when it is full: it has a hard “ceiling.” Instead, it just rams up
against a ceiling, reducing dynamics—the space for sounds to
get louder and quieter—which are an important part of music.
Thus, Luke might benefit from running his beats through
LANDR’s processing before marketing, but his clients would
benefit from using the unmastered versions.
LANDR’s innovation is thus commercial and procedural: it
is cheap enough for Luke to use, and it becomes something
that happens in the middle of the production process as well as
at the end. As Harris Newman explained, LANDR’s version of
music mastering here is more like adding another layer in an
ongoing music production process. In this way, LANDR
works like other labor-saving technologies: it automates a pro-
cess previously done by people that requires effort, skill, and
time. But in so doing, it also potentially transforms expecta-
tions of what unfinished music and audio may sound like. As
Ruth Schwartz Cowan (1983) wrote about labor-saving
devices in a domestic context, they may have eliminated
drudgery and they also increased the standards for cleanliness:
“a senseless tyranny of spotless t-shirts and immaculate
floors,” effectively requiring more work for the same result (p.
216). The comparison is apt: though Cowan is writing about
traditionally feminine gendered domestic labor, it should not
be lost on us that Luke’s work also happens in a home studio,
in the context of amateur production of something that could—
but does not always—enter a money economy. If all producers
were to adopt Luke’s approach, the standards for what an
unfinished beat should sound like would change, and
LANDR’s “savings” of money and labor would cross over
into being an expected expense. The result is that a class of
10 Social Media + Society
cultural producers who did not have to pay for any kind of
mastering now pay for “cheap and easy” automated mastering,
while those who do not pay for it produce music that no longer
sounds as polished or “right” to clients. In other words, auto-
mation often has hidden labor costs for those who use it. It
does not simply simplify the tasks it claims to automate. One
can find many laments of the increasing perfectionism musi-
cians have imposed on themselves as digital tools have become
cheaper, more accessible, and easier to use (see, for example,
Butler, 2014; Provenzano, 2018). It is true that LANDR did
not start this trend, but if its users leaned into the trend further
in terms of the services it provides, LANDR would benefit
Of Chaînes Opératoires and Mastering
As the Luke example shows, mastering is an ever-changing set
of techniques, practices, and technologies, undertaken in a par-
ticular order in a particular social setting. Popularized by Andre
Leroi-Gourhan, (1993) as an extension of Marcel Mauss’
(1973) idea of body techniques, the chaîne opératoire, or oper-
ational sequence, describes a set of repeated and repeatable
actions that involve some understanding of goals, causes, and
effects. Mastering engineers will often speak of their “work-
flow” to describe the sequence of actions they undertake in
mastering a track, combining a set of tasks, judgments, and
technologies. They are not alone: “workflow” has moved from
a term used in logistics to a term widely used in creative indus-
tries and by independent artists. Yet it implies a kind of logisti-
cal mastery that is not often in effect in actual mastering
situations (or other creative situations), which are much more
iterative and dialogical (Fuller & Goffey, 2012, pp. 105–110;
Sterne, 2014). In choosing operational sequence over work-
flow, we aim to suggest mastering music belongs to a wide
world of human activities that are ordered and sequenced, with-
out the analogical baggage of the modern corporation or a for-
mally worked out logic as the assumed background. As an
operational sequence, mastering represents a combination of
body techniques, listening techniques, and technological prac-
tices toward a particular end—mastering. Our use of the term is
meant to highlight a methodological agnosticism regarding
who or what is carrying out the operations. By contrasting the
experience of mastering sessions with people and mastering
sessions with LANDR, we consider the differences between
what mastering can be with a person versus what mastering can
be with an AI-based platform.
Most mastering sessions are “unattended.” The client
uploads a track or album to the mastering engineer’s server,
the mastering engineer works on it, returns it to the client,
they discuss, and usually some revisions are made. However,
attended mastering sessions, where the client is present, are
also part of the business, and for our purposes, made the most
sense as a research approach. Jonathan’s session with Harris
Newman at Grey Market Mastering in Montreal mostly takes
place over a single day. Jonathan arrives at Grey Market in
the morning, having previously uploaded his finished tracks
to a web server. Newman gives all eight tracks a brief listen
sometime before the session, loads them as a single file in his
software, and skips around the record, listening to the loudest
and quietest parts, applying baseline settings for EQ and
compression, modulating them, and comparing them with
one another. Throughout the day-long session, Newman will
compare tracks with one another on the record, making sure
that they sound compatible—not always similar to one
another, but that they work together. He will also frequently
compare the mastered and unmastered tracks at the same vol-
ume, to make sure that he is improving the sound. As issues
come up, they talk them through. They take a lunch break,
walk around Montreal’s Mile End, and eat takeout in
Hotel2Tango’s kitchen. Jonathan has also brought his laptop
to make quick edits on any problems in mixes. At the end of
the day, Jonathan leaves with masters and shares them with
the band. Within 10 days, the project is finished, with a new
order for songs and some minor changes to the relative vol-
ume of different tracks on the album.
Knop works slightly differently. Jonathan and the drum-
mer for the project meet Knop at an apartment in Berlin that
has been transformed into a multiuse space. After tea and
conversation, they transition into working on the record.
They enter the studio and listen to the record together, and
Knop makes notes in his notebook about issues the band
members hear, or things that jump out at him in the mix.
Again, with a laptop present, Jonathan is able to make small
edits to mixes to make Knop’s job easier. Both mastering
engineers begin from some initial settings that they know
tend to work for the kind of music at hand, and then they
make small tweaks, often after overemphasizing a particular
frequency or timbre to bring it out, and then boosting or cut-
ting as needed. They also apply various sweetening tech-
niques to the mixes, working with the stereo image, front to
back sound, dynamics, harmonics, and phase relationships.
Knop makes “draft” masters of a couple tracks in their pres-
ence, and then they leave for the day (but not before also
having lunch and more conversation). Approximately 2
weeks later, he sends masters of the full record, after which
he and the band members correspond regarding changes;
there is some back and forth. Final mastering takes a few
more weeks between delays with the band, Freddy’s other
projects, and the Christmas holidays.
That both engineers work at the album level is important.
LANDR is designed to work primarily at the level of the
track. For most of our study, it was only possible to master
individual tracks on LANDR; there was not any album
option. But in July 2018, as we were submitting this essay for
initial review, they added an “album” option. It appears to
only be an add-on to their process and not a rethinking of it.
In Fall 2018, Jonathan uploaded the unmastered versions of
the album mastered by Freddy to test it, and it was clear that
even with this option, LANDR does not frame mastering in
Sterne and Razlogova 11
terms of albums: all the songs Jonathan uploaded were pro-
cessed to be at the same loudness, so that an acoustic guitar
ballad actually sounded comparatively louder than a down-
tuned rock song with heavy distortion on all the instruments
and voices. This is the exact opposite of the desired result,
and something a mastering engineer would hear and begin
adjusting for in their initial pass through the songs. The lev-
els on the ballad were lower before mastering, so LANDR
actively altered the overall levels and dynamic range to flat-
ten it out in an unhelpful way. Compared with Knop’s work,
LANDR’s album option flattened out the music, giving it
consistency from track to track but the wrong kind—consis-
tency at the expense of musical coherence. In its instructions
for album mastering, LANDR also offloads album typical
mastering engineer tasks—like track timing and pauses,
playlisting, and fade-ins and fade-outs—on to users. Again,
LANDR’s claims to automate a process through AI hide the
ways in which LANDR actually creates labor for its users.
In discussions about his practice and its relation to
LANDR, Harris questioned whether LANDR is really mas-
tering if it works on single songs only. His sense of what
mastering is comes from the “album” moment of musical
history, the form of mastering that took shape in the 1990s
with the wider availability of digital tools. For him, a master-
ing engineer works with finished tracks in their relation to
one another for the purpose of creating an album. Mastering
involves not only the songs themselves but also the relations
between them: how they sound together, how they flow from
one to the next, fade-ins and fade-outs, pauses between
songs, and timing of the entire record. This is a historically
specific understanding of mastering, since mastering engi-
neers have in the past worked on single tracks as well.
Newman began his work in the era of using specialized soft-
ware to assemble records and compact discs (CDs), where
the average musician could not make a CD at home, and
where production plants had rigorous requirements for the
formatting of a master disk for reproduction. So for him,
mastering requires a concept of the album. LANDR is, in his
words, “just another layer of production” (H. Newman,
Interview by J. Sterne, August 14, 2017). As Newman would
have predicted, Jonathan’s work with LANDR diverged
wildly from working with the two mastering engineers. For
one thing, it did not have to be scheduled, and it was not
necessarily an event. He was able to run mixes through
LANDR before they were finished to share drafts with band
members. He was able to work and audition at his home stu-
dio, which is also acoustically treated but not as thoroughly
as either of the mastering studios. In this way, working at
home was both an advantage (convenience) and a disadvan-
tage (in terms of critical listening). He interacted with
LANDR entirely through its web interface and email.
Inasmuch as it is a music company, LANDR takes its inter-
face cues from other web-based music applications, especially
major music recommendation and recognition services, such as
Shazam and Spotify. These cues are visible in its strategies for
self-presentation and algorithm development. As of this writing
LANDR’s website conforms to the modern aesthetics of clean,
sparse web design with lots of white space. The interface fol-
lows typical conventions common for web applications that
handle files in a myriad of ways. In this way, it looks more like
any other cloud file service—like Dropbox or—and
less like an audio product. Instead of the skeuomorphs of
knobs, faders, flashing lights, and pictures of wood panels that
one often finds on commercial audio software, they aimed to
make it more like file transfer services. When we asked about
the reason for a clean interface that does not allude to tradi-
tional music tools, Evans said, “How do you create a new
behavior that isn’t threatening to people? We did a lot of think-
ing about interfaces that are not going to feel like ‘oh my god,
what am I doing here?’” (J. Evans, Interview by authors,
August 24, 2016).
Figure 4 shows LANDR’s user page upon sign in. It con-
tains all previous uploaded tracks, as well as the possibility
to sort by project. To master a track, a user drags and drops
or clicks the big blue “master” button and selects a track
from a folder on their computer. In this way, LANDR encour-
ages its users to treat their audio like any other kind of data,
and audio mastering like any other kind of data service. Yes,
LANDR requires a specific format (as do the mastering
houses generally), but it is the protocols of uploading, orga-
nizing, and making choices about the music where the mas-
tering experience is entirely different.
Figure 5 shows the mastering interface. Users can audi-
tion the sounds before selecting and paying for a mastered
track, and choose from three “intensities,” which they
describe in terms of overall loudness, but might also be
understood in terms of limiting dynamic range: in our
hydraulic metaphor, higher “intensity” fills the cup closer to
the rim. What LANDR is doing under the name of “inten-
sity” is applying compression and dynamic equalization,
along with other processes, to the uploaded file. For an actual
mastering engineer, there would be many microscopic sonic
choices and adjustments to make along the way. For a
LANDR user, there are only three choices the user makes,
and in making those choices, they do not see what adjust-
ments LANDR actually makes to the recording. Its opera-
tional sequence cannot be known to users, between corporate
secrecy, ever-changing back ends, and the status of algo-
rithms as golem-like assemblages. In a companion piece, we
explore this more fully (Sterne & Razlogova, forthcoming).
But for now, we simply note that while LANDR could work
entirely by an ML process, it is much more likely that ML is
simply used in one small part of the process. In this, they are
not alone. Ozone 8, mastering software offered by Izotope,
one of LANDR’s competitors, is also trumpeted as an AI
application. Yet, when Jonathan attended a demonstration of
the software at the National Association of Music Merchants
(NAMM) in January 2019, it was clear that while engineers
may have used ML in the design of the application, it was not
actually doing any ML when it was processing audio on a
12 Social Media + Society
Figure 4. Screenshot of Jonathan’s LANDR account page, as of August 20, 2018—note that interfaces like this change frequently;
interface images are necessarily snapshots. Photo by author.
Figure 5. Screenshot of LANDR Mastering screen from Jonathan’s work.
Sterne and Razlogova 13
user’s computer—it was processing the audio like any other
program would. Jonathan confirmed this with some pointed
questions to the presenter afterwards, who conceded this
point, even though he had used the phrase “now it’s doing
some machine learning” while waiting for the software to
run a routine during the presentation. LANDR also clearly
plays on this ambiguity: AI becomes synecdochic for every-
thing the software does, and in so doing, works more like a
marketing term than an explanation of anything. Beyond the
critique of hype is a more serious methodological point: as
scholars, we need to be careful to place AI operations within
the organizational and cultural contexts, lest we overestimate
its reach and impact apart from everything else.
Contrast LANDR’s operational sequence with the
actions and decisions available to Freddy Knop at Listeners
Mastering. Figure 6 shows one of the three equipment panels
available to him in real time (along with all the parameters
inside his computer). The pictured rack shows two equaliz-
ers, which allow for many precise changes to the frequency
balance across the audio spectrum, ranging from the subtle to
the extreme; the top device is a compressor for adjusting the
dynamic range of the audio and making separate sounds gel
with one another. A mastering studio like Listeners is set up
to present a mastering engineer with dozens, maybe hun-
dreds or thousands, of choices from second to second, but to
make the most common choices (or ranges) available quickly.
Mastering involves making all of these tiny choices in real
time. In contrast, LANDR’s Mastering interface presents its
user with a single choice consisting of three options.
Understood in terms of operational sequence, Freddy is a
special kind of listener and musician, and the work of signal
processing is subordinate or predicate to listening. If LANDR
listens, its listening must be predicate to data processing, and
its interface foregrounds its understanding of music as data
first, music second.
Once a user selects one of LANDR’s three options, it
takes a few minutes to receive a mastered recording. Figure
7 shows the mastered track view, which is reminiscent of the
SoundCloud waveform display, allowing for comparison of
the uploaded and mastered track, sharing of the track, and
moment-by-moment commentary on the track by multiple
users. The model here is other cloud-based collaborative
platforms designed to provide opportunities for remote dia-
logue and co-work.
In a certain sense, LANDR’s interface is ideological in
the way every other software interface is ideological: the
representational strategies of computer interfaces are
designed to conceal some processes and decisions, while
drawing attention to others; to mark some actions and ori-
entations as “preferred” or “not preferred.” As Wendy Chun
(2011) argues, “from ideology as false consciousness to
ideology as fetishistic logic, interfaces seem to concretize
our relation to invisible (or barely visible) ‘sources’ and
substructures” (p. 59). Interfaces like LANDR attempt to
construct a seamless unity out of a set of arbitrarily con-
nected processes. As such, they represent their preferred
chains of operations as “natural” for the intended user (even
if in actual use, there is resistance to the scripts they set
out), and they suggest analogies to understand their use. As
they describe the tasks they lay out before the intended
user, they also use their description of the world to make
prescriptions regarding how it should be (Bourdieu, 1991).
By referring to things that musicians use that are not like
studio technology, such as file transfer services, and by rig-
orously following mainstream web design conventions,
LANDR has designed an interface that calls attention to
itself only to suggest that mastering is as straightforward as
other things artists might do with completed audio files
online. In other words, it frames mastering as one kind of
commoditized service (web file service) that wholly sub-
sumes another (audio mastering), while emphasizing ease
and familiarity of use. It at once aims to demystify master-
ing by making it accessible and to re-mystify mastering by
creating new associations for the process in the mind of the
user while hiding the inner workings of the process as much
as possible.
While mastering engineers draw from the history and tradi-
tions of audio engineering, LANDR draws from the tradition of
web-based audio applications from Winamp on down (Morris,
2015). While users may interact with the scripts set out for
them in myriad ways, the overall effect of LANDR’s approach
is to transform the status of mastering from something
whose inner workings are obscured for the user because of the
structure of the audio industry to something that is obscured
from the user because of the inner workings of its status as a
Figure 6. A rack of gear at Listeners Mastering in Berlin. Photo
courtesy of Freddy Knop and used with permission.
14 Social Media + Society
web-based software service. This is a significant change. It is
“technical transcoding … that nevertheless coexists with an
exceedingly high level of ideological fetishism and misrecog-
nition” (Galloway, 2012, p. 60). Software and mastering oper-
ate as meshes of discourses, materials and practices that aim to
shape a corner of the auto-technical universe. Certainly, record-
ing and mastering studios like the ones Jonathan has visited are
also fetishistic and ideological in terms of how they set them-
selves apart from other spaces of everyday life: they use the
visual rhetoric of mid-20th century electronics, evoking giant
mainframe computers, telephone switchboards, or space travel
(Meintjes, 2003, pp. 72, 84). But these are two totally different
stories about music and technology: LANDR tells a story of
consumer web services and music as data that represent sound;
the blinking lights and psychocosmetics of Grey Market and
Listeners tell a story about control over sound and music as
vibrations in the air and as electricity that represents sound.
Frequencies Have Meanings: LANDR
Versus Two Bass Hermeneutics
LANDR’s approach to control over sound also differs mark-
edly from that of mastering engineers, and further elucidates
what it means to delegate an aesthetic process to an ML-based
platform. According to Evans, as well as the available evi-
dence online, a large cross section of LANDR’s users mix
music in home studios or other spaces that may not have much
acoustic treatment. One of the issues for people who work in
this kind of space is that they cannot hear or properly manage
low frequencies. Because of their relatively longer wave-
lengths, low frequencies are especially prone to building up or
canceling out one another in the small and imperfect spaces of
amateur audio engineers. This means that LANDR gets many
recordings with bass problems. If there is too much bass,
LANDR clamps it down, disciplines it, and makes sure it does
not overwhelm the track or blow up the speakers of anticipated
future listeners. LANDR cannot tell the difference between a
bass drum, a synth bass, a bass drop, a bass voice, a bass clari-
net, a bass guitar, or simply “unruly” bass frequencies.
When Jonathan uploads a rock mix with a distorted bass
solo, LANDR clamps down hard. It shoves it back into the
music, flattening it out. In a way, this action makes sense in
context. Bass solos are relatively unusual in rock songs; they
break with the customary tonal palette of the style. In most
music, bass and low-frequency sounds are relatively consis-
tent. It is statistically more likely that a mix uploaded to
LANDR has bass problems than a bass solo. When it gets the
song, LANDR analyzes the sudden and temporary boost in
low-midrange frequencies in a bass solo, treats it as something
that is not supposed to happen in the music, and, therefore,
reacts to it as a mistake, as a problem to fix. Over the course of
several hours, in two separate sessions, Jonathan tries to game
the algorithm: boost the bass more, LANDR clamps down
harder; change the frequency balance, LANDR smooths it out.
Nothing he can do will produce the desired result with LANDR.
LANDR does not provide any specific feedback on mixes,
which exacerbates the problem here. Rather, its approach to
mixes is based on feedback loops in its own operations, which
it cannot explain to users (Sculley, Phillips, Ebner, Chaudhary,
& Young, 2014, p. 3). A mixing engineer has to guess what
LANDR will do, knowing only the inputs to and outputs from
the mastering process. They can try to game the result by
reverse engineering what happened, but they can only guess.
It is impossible to tell just listening whether the software is an
ML-based process that is doing the same thing to the audio
because it detects different iterations of the same phenome-
non, or whether it is simply applying a preset and Jonathan’s
changes are not big enough to trigger an analysis that would
yield selection of another preset. There is not enough audio
evidence to deduce a cause or causes, and no amount of fid-
dling on Jonathan’s part changed that: opacity is actually a
constitutive feature of systems like LANDR (Burrell, 2016).
Mastering engineers can also be opaque in their decision-
making process, but that opacity matters in a different way. A
few months after his bass solo experiments at home, Jonathan
is at Listeners Mastering in Berlin. The same song with the
bass solo that gave LANDR trouble plays over the speakers.
Knop, a bassist himself, immediately hears the solo, tweaks a
Figure 7. Screenshot of a mastered track with options for commentary, sharing, download, and release, again from Jonathan’s work.
Sterne and Razlogova 15
couple knobs on the EQ to bring it out more clearly as the
section plays over and over, and adjusts the compression for
the whole song slightly. After a few minutes of working like
this, the bass solo rings through loud and proud. The drums,
however, just are not working. We listen together, he makes
some suggestions, Jonathan makes a few adjustments to the
mix on his laptop and passes the track back to Freddy for
another pass through the software. This time it sounds more
dynamic and exciting to the three of us present. Knop’s deci-
sions are still ultimately opaque to Jonathan: he did not leave
the session able to reproduce Knop’s series of decisions and
actions himself. But because the situation was dialogic, and
because Knop heard the music as music, rather than as data,
he was able to make adjustments that were more aesthetically
satisfying to Jonathan and his bandmates. In this case, opacity
did not matter for achieving the desired aesthetic results.
Although the two cases of back and forth are superficially
similar, they reflect two very different operational sequences.
In one case, the mixing engineer must go through a series of
trial and error scenarios to produce a mix appropriate for the
algorithm to produce a desired result. The engineer cannot
talk to the algorithm, and the algorithm cannot provide a sat-
isfactory explanation for the problem the engineer hears, so
experimentation is the only option. Even if the engineer
could talk with the people who produced the algorithm, they
may or may not be able to explain its decisions. And even if
that were the case, the algorithm might make a different deci-
sion when the revised track was uploaded. It would be wrong
to render Jonathan’s interaction with Freddy as some kind of
transparent revelation of mastering techniques: it most cer-
tainly was not. But because of how the mastering process
was set up, it was possible to achieve a desired result.
LANDR may do different things to the same track on dif-
ferent days, but that would depend on how ML is imple-
mented in their process (e.g., actually transforming the audio
rather than selecting collections of presets), and whether their
engineers have made a change in the ways LANDR processes
audio since the date of the previous attempt. A human master-
ing engineer might also make two slightly different decisions
on different days for the same piece of music, might also
begin from “preset” ideas of how to process aspects of the
sound that are built into their standard operational sequences
when first working with new recordings, and different engi-
neers will make different judgments on a given track (in fact,
the variety of mastering styles is considered a good thing for
the industry and for musicians). But this is not to overstate the
similarity. It is common for mastering engineers to speak of
knowing when to leave a track alone or even to undo their
work, as when mastering engineer Bob Ludwig, describing
his work on Bruce Springsteen’s Nebraska, says “I corrected
the azimuth and speed of the tape, but Bruce liked it left
alone” (Ketterer & Ludwig, 2015). In contrast, LANDR will
never leave a track alone. In user tests, they found that the
software had to do something to the unmastered track in order
for users to trust it (J. Evans, Interview by authors, August 24,
2016; Piotrowska, Piotrowski, & Kostek, 2017).
The lessons learned through working with mastering engi-
neers are transferable to work with other mastering engineers.
For another mix, Jonathan kept increasing the volume of a
somewhat veiled drum fill at the drummer’s request. At Grey
Market Mastering, Newman heard it and—unprompted—
immediately said it sounded wrong to him, and explained why.
Together, mixing and mastering engineers were able to find a
way to highlight the fill without it overwhelming the rest of the
music. Prior work with Harris shaped how Jonathan worked
with low frequencies in general. When he arrived at Listeners
in Berlin, Knop had less work to do on the low end of that mix
because of Jonathan’s prior learning about how to mix sound
with mastering in mind. Thus, we see two kinds of learning
here: both involve trial and error, both involve trust. But mas-
tering with people relies on personal, pedagogical relationships
and shared aesthetic understandings. The skills learned here are
more likely to translate because the engineer has a better under-
standing of cause and effect. In LANDR’s case, the mixing
engineer may get better at mixing for LANDR, but has no way
of testing or confirming their understandings of cause and
effect, and thus is not in a good position for their skills to be
useful in other contexts. This works perfectly with LANDR’s
platformization strategy but does nothing to prepare its users
for interactions with mastering engineers—or other mixing
engineers or musicians—in the future. Joseph Klett (2016)
describes the deliberate obfuscation of cause-and-effect by
software like LANDR as “baffling,” by which he means that
the user is no longer able to fully “define the situation” in which
the software acts, as it produces sound “made meaningful-to-
measure.” LANDR refuses to explain its process fully, baffling
its users both in the sense of confusing them about the process
to which their audio is subjected, and separating them from that
processwhile attempting to locate them into a particular set of
economic and social relationships. The difference between
using LANDR and using a mastering engineer is not the isola-
tion of software vs. the interaction of interpersonal communi-
cation (since working with a mastering engineer can also be
highly impersonal), but rather the balance of relationships and
agencies in a given situation. Both the company and the people
“serve” their clients, but LANDR serves its own platform;
Newman and Knop serve their scenes. In part, one could argue
the difference is between artisanal and industrial capitalism in a
cultural domain. But this would be to isolate mastering too
much from other cultural processes: it would be more accurate
to say that what Newman and Knop do feels more like artisanal
capitalism in the highly customized spaces and experiences
they provide; LANDR’s lack of customization feels more
industrial, but also more, for lack of a better term, platformy.
The relationship between mastering engineers and LANDR is
not a John Henry-like battle between man and machine. To tell
the story in that way is to obscure the degree to which AI for
music is still intertwined with human action and decision-
making. After much agonized comparison of freeform DJs and
16 Social Media + Society
recommendation systems, in 2014, Spotify quietly began to
rely on human curators in creating many of its most popular
playlists. It is now a standard practice in the industry, adopted
by Apple Music and Google Play (Ugwu, 2016). More than
once, app creators had to tweak their algorithms to adapt to
human expectations. As we have seen, the LANDR algorithm
has to master a track even if it determines that a change is not
necessary, just because its users expect a change. Likewise,
Apple had to change its algorithm for random song plays in
iTunes because users were upset when the same song came up
twice in a row—a normal consequence of true randomization
(Levy, 2006).
LANDR is not a stable entity; and neither is mastering.
Venture capital has led the company to expand in different
ways. Requests from users led it to take on album mastering in
form if not in substance. It has used the local music scene as a
source for talent, as a lever for legitimation, and as a place to try
out different identities. Its interface and advertising rhetoric
have undergone changes during the period of our study, and we
expect that its back end has undergone changes as well, though
we cannot prove that. The rhetoric around AI and labor is that
it automates jobs away from people, but this is not what has
happened with LANDR, at least not yet. Rather, it has morphed
the definition of mastering, possibly expanding it, though
potentially in problematic ways. The service may, in time, take
jobs from low-level mastering engineers. It may also find other
markets as its service improves, like mastering for film, TV, or
advertising (at the time of our study, LANDR did offer unad-
vertised services to larger firms). But no music mastering engi-
neer we met in this study expressed any concern about LANDR
as a threat to their business. Larry Crane noted the same indif-
ference as well in his world. The app gives mastering options to
amateur and cash-strapped musicians who otherwise would not
have them, at the same time as it reshapes standards and expec-
tations for their demos and samples. Independent artists have
participated in the creation of the algorithm as LANDR
employees and collaborators, but with only temporary financial
gain and with little credit given in the end, as with other new
media companies. And any individual artist may use or not use
LANDR depending on availability of funds and aesthetic goals
for a particular project. Artists and engineers who have access
to a human mastering engineer still have a much better chance
to learn and improve in their craft in a way that makes sense for
all future work; LANDR teaches engineers to produce better
mixes for LANDR. The mastering industry is also changing for
reasons that have nothing to do with LANDR: when Jonathan
started recording, mastering involved conversion from one for-
mat to another and had no online component. Today most
mastering houses have integrated the internet into their busi-
ness in one way or another. High quality software and easier
access to knowledge about sound and acoustics has theoreti-
cally reduced barriers to entry into the business, while shrink-
ing major label budgets has squeezed the top end of the
business. But mastering shows no sign of being automated
out of existence, despite the claim of services like LANDR. If
anything, they are offering mastering to clients who might
otherwise not paid for it at all.
The LANDR story suggests a set of questions we
should ask as AI-enabled applications move further into
aesthetic domains. Like algorithms that correct photo and
video images, mastering algorithms (and music recogni-
tion algorithms) seem unknowable—“black boxed”—if
we zoom in too closely on their operational protocols, but
their social existence is legible with a little analysis and
comparison. They exist within operational sequences that
go far beyond the simple facts of signal processing. AI
and ML are often represented as complete breaks with
prior technical practice, and in some spheres, they might
be. But we should be wary of exaggerating the role of AI
in isolation from other factors. From the standpoint of cul-
ture, as of yet AI has not produced a major paradigm
change, and it requires analysis through the already avail-
able tools of media studies, science and technology stud-
ies, and more generally the tools of the humanities and
social sciences. Researchers should not assume that the
most important expertise for the critical study of AI is in
the internal workings of ML.
Inasmuch as online platforms have tended toward concen-
tration of ownership and market share, we should attend to
the political, technical, industrial, and cultural stakes of
removing aesthetic decisions from the cultural contexts in
which they occur and locating them instead in a “platform”
context. After all, companies sell music as a product or ser-
vice; people make music for all sorts of reasons, and the
making can be an important part of the social and economic
life of a scene or community, as the Montreal scene and both
Berger’s and Knop’s careers show. Like art, film, games, lit-
erature, journalism, and countless other cultural practices,
music is more than “data” to be processed, “content” to be
shared, though of course it can be that as well. Larry Crane’s
main concern about LANDR seemed to be that it devalues
the creative process and the people involved in making
music; in this, they are hardly alone among new media busi-
nesses. LANDR’s organizational strategies are focused
around return-on-investment for venture capitalists and
around a diversified platform of services designed to maxi-
mize user engagement and generate a valuable data set. At
the same time, LANDR is clearly of use to many of its
users—including Jonathan. But that utility comes as much in
spite of LANDR’s organizational and technical values as
because of them. Far from the spectacular rhetoric around AI
as an emergent form of nonhuman agency, in learning from
LANDR we find a very a familiar set of agencies—financial,
corporate, technical, musical, and human—hard at work in a
new setting.
We would like to thank everyone who took time to talk with us
about our research and educate us about their work and art. For
comments on drafts, we thank Carrie Rentschler, Nick Seaver,
Sterne and Razlogova 17
David Suisman, Andy Stuhl, members of the CATDAWG and Bits,
Bots, and Bytes working groups at McGill, as well as audiences at
the Association of Internet Researchers, Penn State, Humboldt
University, Stanford, Vanier College, and AI Now.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect
to the research, authorship, and/or publication of this article.
The author(s) disclosed receipt of the following financial support for
the research, authorship, and/or publication of this article: This work
was supported by the Social Sciences and Humanities Research
Council of Canada.
1. Both records are available as full albums with liner notes,
which can be downloaded from the bands’ Bandcamp sites.
These can also be found on streaming services.
2. Throughout the article, we use independent, self-employed, DIY,
and amateur as related terms denoting limited economic means
of producing and publishing music, while we also pay atten-
tion to the ways “indie” and “DIY” have been used to market
certain profitable genres and technologies, including LANDR
(Bell, 2014; Hesmondhalgh, 1999). Montreal’s Anglophone
and Francophone music scenes do overlap, but the industrial
structure of Quebec francophone music is somewhat different
because of protocols for state funding and Montreal’s place at
the center of North American Francophone media culture.
3. We have changed his name to preserve anonymity.
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Author Biographies
Jonathan Sterne is James McGill Professor of Culture and
Technology at McGill University.
Elena Razlogova is associate professor of History at Concordia
... This work is ideal for large-scale production robotics. Smaller cobots also perform machinery tendering and loading/unloading operations for relatively minor production activities (Sterne & Razlogova, 2019). ...
The advancement of SMEs is accelerated by technological expansions using blockchain technology in the Industrial Revolution (IR) 4.0 era. Based on current trends in AI and blockchain technology, this study proposes that the distance between entrepreneurs all over the world and their potential workers may be greatly decreased to virtually real-time. A secondary literature review is carried out in order to identify the key developments in IR 4.0 technologies in the SMEs industry, as well as the potential trend that will lead the business sector. The adoption of AI and blockchain technology in the IR 4.0 technologies is projected to make seeking treatments overseas more reasonable, accessible, and health records readily available on a real-time and protected basis. However, it is necessary to highlight that the expansion of SMEs raises the eyebrows of society from the security, social, and economic viewpoints.
... Algorithmic online mastering services work in the way human mastering would by optimizing a recording for dis-PAPERS STUDY OF MUSIC BY ENGINEERS AND AUTOMATED SERVICES tribution and improving the given material with a final round of signal processing. However, individuals such as Harris Newman, who has mastered numerous albums since 1998, question if instant mastering is truly mastering [2]. Decision-making based on massive amounts of data processing, predetermined musical targets, and online streaming competitiveness may ignore crucial elements in a piece of music that a human mastering engineer could identify as significant. ...
Technological advances in music and audio engineering have brought forth a new method of audio mastering in the form of online automatic mastering services. However, many claim that the results from automatic mastering services are inferior to the work of professional human engineers. The presented investigation explores the perception of mastered products by popular mastering services found online. Music was submitted for mastering provided by two human mastering engineers and two automatic mastering services. In a listening test, subjects were asked to identify human-mastered samples. Later, subjects were asked to provide preference rankings among human-mastered and instant-mastered samples. Furthermore, objective parameters pertaining to timbre and spectral energy distribution were calculated from stimuli. Subjects were unable to consistently identify human-mastered musical samples. Preference towards human-mastered samples was observed from jazz excerpts but not from rock excerpts. These results show a partial support for claims of human mastering superiority based on preference. This study provides a new perspective on the perception of content from human and instant mastering, which may offer a first step to understanding many differences between the two services.
... TI's DSP chip has the characteristics of high performance, good stability and a wide variety, mainly including fixedpoint and floating-point. The advanced power management technology of TMS320C55x series DSP can automatically turn off idle peripherals, memory and core functional units, extending the battery life [17]. For the music training assistant system, low power consumption and real-time performance are the main performance requirements. ...
In order to improve the input accuracy and response speed of music training, this paper designs an intelligent assistant system. The architecture is divided into infrastructure layer, data layer, application layer and presentation layer. In the hardware design, the combination of ARM and digital signal processor (DSP) is used to realize the interaction between data analysis and human and interface. In the software design, cepstrum algorithm is used to extract cepstrum features of music signals, linear smoothing algorithm is used to filter, dynamic time warping method is used to match patterns, and radial basis function algorithm is used to output the results. Thus, the overall design of the music-assisted training system is completed. Experimental results show that the signal-to-noise ratio of music signal transmission is more than 14dB, the accuracy is higher than 99.5%, and the response speed of serving 240 users is only 1s. The system has strong operability and good performance of music assistant training.
... In order to balance both advertisers and consumers, the debate on the strategic use for AI is structured in terms of the marketing of 4Ps/4Cs [23]. The Thinking AI can be used to classify rivals on a well-defined or external market, and to gain an insight into the competitive advantages of a product [24]. The principal difference between market research and client comprehension is that consumer thoughts, feelings, desires, and attitudes also contain emotional data. ...
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Recording production is a complex, multistep, typically collaborative process that entails a shifting set of individuals inhabiting changing roles within spaces that house considerable amounts of specialized technology. As these roles and technologies feature prominently in the aesthetics of Anglophone and Francophone popular music, they have been studied within such milieus for the longest period. This scholarship tends to understand the creative act as either the result of a prominent individual or something determined by the technologies used within studios. However, recent ethnomusicological scholarship has shown it is much more difficult to clearly situate agency within recording production, echoing theories of agency developed within the fields of anthropology and science and technology studies. The myriad uses of and significant cultural work that recordings can do show that one can’t assume that the goal of production work is simply to produce an aesthetic art object. For example, recordings can serve as a form of social action, and in many milieus the social values of the production process matter more than the financial success of the product. Ultimately, a nuanced consideration of agency within recording work produces important findings on the concept of creativity and the creative act.
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Interpersonal interaction through vocal music language has become an important channel for cross-cultural social life. How to skillfully use vocal music language to stimulate the audience's emotional arousal and trigger their immersive experience has become a research focus in the field of music. At present, the academic research on vocal music language is diversified, and has explored the path of vocal music occurrence and its development direction under specific circumstances. However, the process of immersive experience arousal from the perspective of "music" language and "lyrics" language is a hidden code study. In order to comprehensively consider various styles of vocal music, we selected the music of three different singing styles including folk style singing, bel canto and popular singing as experimental materials to study the arousal process of the audiences' immersive experience of vocal music language when listening to music. The results indicate that both "music" and "lyrics" language perceptions exert a significantly positive impact on the audience's emotional arousal and immersive experience, and in addition, arousal plays a significant mediating role between vocal music language and immersive experience. Therefore, by means of clarifying the internal logic of the audience's immersive experience from the perspectives of "music" and "lyrics" in the vocal music language, this study provides new theoretical insights for the applicable boundary of telepresence in the vocal music field, facilitate operators to design on-the-scene vocal music art environment to convey the emotional connotation of vocal music, and further promote the development of the vocal music field.
This chapter examines Montreal’s contemporary independent and experimental music scenes in relation to the perceived sense of cultural and industrial isolation felt by local independent music makers between the 1980s and the early 2000s. This chapter argues that the work of micro-independent record labels such as Constellation Records and DAME and other small-scale scene infrastructures were instrumental in breaking out of industrial and cultural isolation by nurturing the activities of their own artistic communities while creating connections and bridges with similar scenes in North America and Europe. These efforts contributed to foster greater dialogue and collaborations between the Francophone and Anglophone factions of the scene during the 2000s, and helped establish the aesthetic and logistical elements that became associated with the contemporary Montreal independent music scenes.
This chapter examines the MUTEK festival and Arbutus Records as two key infrastructures supporting the experimental electronic music scene in Montreal. Both institutions, in their own specific ways, showcase and support local electronic music talent and have become emblematic of the musical identity of the city. On one level MUTEK and Arbutus appear to exist within opposite the spatial and historical trends in the artistic development of Montreal in the late twentieth century/early twenty-first century. On another level, they are both embedded within wider developments of music policies in Montreal: MUTEK has grown increasingly connected to municipal planning for cultural tourism and Arbutus is connected to informal Do-It-Yourself underground music practices. Therefore, this chapter argues that MUTEK and Arbutus Records exemplify contrasting yet interlocked trends surrounding the policing of electronic music in the city.
This chapter examines the development of electronic music scene in Łódź, which is the third largest city in Poland, focusing on the period from the 1990s to the present day, against the backdrop of Łódź’s political and social history. The chapter pays attention to artists connected to the city, the spaces in which they performed, such as clubs and music festivals and the genres which took root in Łódź, particularly drum and bass. It also presents the legal framework and initiatives of local politicians, which affected electronic music produced and consumed in Łódź. It also tests the hypothesis that there is a specific fit between the city and the type of electronic music which has flourished there. The authors draw on the research on electronic music, scenes and the concept of music ecologies.
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This paper traces the infrastructural politics of automated music mastering to reveal how contemporary iterations of artificial intelligence (AI) shape cultural production. The paper examines the emergence of LANDR, an online platform that offers automated music mastering, built on top of supervised machine learning branded as artificial intelligence. Increasingly, machine learning will become an integral part of signal processing for sounds and images, shaping the way media cultures sound, look, and feel. While LANDR is a product of the so-called ‘big bang’ in machine learning, it could not exist without specific conditions: specific kinds of commensurable data, as well as specific aesthetic and industrial conditions. Mastering, in turn, has become an indispensable but understudied part of music circulation as an infrastructural practice. Here we analyze the intersecting histories of machine learning and mastering, as well as LANDR’s failure at automating other domains of audio engineering. By doing so, we critique the discourse of AI’s inevitability and show the ways in which machine learning must frame or reframe cultural and aesthetic practices in order to automate them, in service of digital distribution, recognition, and recommendation infrastructures.
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The advent of digital distribution platforms has removed middlemen from the musical supply chain-better known within the industry as the process of "disin-termediation." Often celebrated, disintermediation is understood to have lowered the barrier to entry into the music industry, following the sudden rise in access to marketplace and distribution platforms. However, this increased access to the means of distribution comes with increased responsibilities; musicians not only write, record and produce their own music, in the digital era, they are charged with the added responsibility of promoting, circulating and distributing their music. To understand this disruption, this essay presents the conceptual framework of the artist-as-intermediary. Suturing the artist to the intermediary, the artist-as-intermediary articulates the ways in which the responsibilities formerly held by music label executives-promoters, managers, A&R representatives-have not disappeared with disintermediation but have instead been downloaded onto the musician. To more fully illustrate this phenomenon, this essay proffers a brief case study of marketplace and distribution platform Bandcamp and indie musician Car Seat Headrest.