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Automatic Music Transcription: An Overview



The capability of transcribing music audio into music notation is a fascinating example of human intelligence. It involves perception (analyzing complex auditory scenes), cognition (recognizing musical objects), knowledge representation (forming musical structures), and inference (testing alternative hypotheses). Automatic music transcription (AMT), i.e., the design of computational algorithms to convert acoustic music signals into some form of music notation, is a challenging task in signal processing and artificial intelligence. It comprises several subtasks, including multipitch estimation (MPE), onset and offset detection, instrument recognition, beat and rhythm tracking, interpretation of expressive timing and dynamics, and score typesetting.
Automatic Music Transcription: An Overview
Emmanouil Benetos Member, IEEE, Simon Dixon, Zhiyao Duan Member, IEEE, and Sebastian
Ewert Member, IEEE
The capability of transcribing music audio into music
notation is a fascinating example of human intelligence. It
involves perception (analyzing complex auditory scenes), cog-
nition (recognizing musical objects), knowledge representation
(forming musical structures) and inference (testing alternative
hypotheses). Automatic Music Transcription (AMT), i.e., the
design of computational algorithms to convert acoustic music
signals into some form of music notation, is a challenging task
in signal processing and artificial intelligence. It comprises
several subtasks, including (multi-)pitch estimation, onset and
offset detection, instrument recognition, beat and rhythm track-
ing, interpretation of expressive timing and dynamics, and
score typesetting. Given the number of subtasks it comprises
and its wide application range, it is considered a fundamental
problem in the fields of music signal processing and music
information retrieval (MIR) [1], [2]. Due to the very nature
of music signals, which often contain several sound sources
(e.g., musical instruments, voice) that produce one or more
concurrent sound events (e.g., notes, percussive sounds) that
are meant to be highly correlated over both time and frequency,
AMT is still considered a challenging and open problem
in the literature, particularly for music containing multiple
simultaneous notes1and multiple instruments [2].
The typical data representations used in an AMT system
are illustrated in Fig. 1. Usually an AMT system takes an
audio waveform as input (Fig. 1a), computes a time-frequency
representation (Fig. 1b), and outputs a representation of pitches
over time (also called a piano-roll representation, Fig. 1c) or
a typeset music score (Fig. 1d).
In this paper, we provide a high-level overview of Automatic
Music Transcription, emphasizing the intellectual merits and
broader impacts of this topic, and linking AMT to other
problems found in the wider field of digital signal processing.
We give an overview of approaches to AMT, detailing the
methodology used in the two main families of methods,
based respectively on deep learning and non-negative matrix
factorization. Finally we provide an extensive discussion of
open challenges for AMT. Regarding the scope of the paper,
we emphasize approaches for transcribing polyphonic music
produced by pitched instruments and voice. Outside the scope
of the paper are methods for transcribing non-pitched sounds
such as drums, for which a brief overview is given in Section
Authors in alphabetical order.
EB and SD are with the Centre for Digital Music, Queen Mary University
of London, UK. e-mail: {emmanouil.benetos,s.e.dixon}
ZD is with the Department of Electrical and Computer Engineering,
University of Rochester, NY, USA. e-mail:
SE is with Spotify Ltd, UK. e-mail:
EB is supported by a UK RAEng Research Fellowship (RF/128).
1Called polyphonic music in the music signal processing literature.
IV-F, as well as methods for transcribing specific sources
within a polyphonic mixture such as melody and bass line.
A. Applications & Impact
A successful AMT system would enable a broad range
of interactions between people and music, including music
education (e.g., through systems for automatic instrument
tutoring), music creation (e.g., dictating improvised musical
ideas and automatic music accompaniment), music production
(e.g., music content visualization and intelligent content-based
editing), music search (e.g., indexing and recommendation of
music by melody, bass, rhythm or chord progression), and
musicology (e.g., analyzing jazz improvisations and other non-
notated music). As such, AMT is an enabling technology with
clear potential for both economic and societal impact.
AMT is closely related to other music signal processing
tasks [3] such as audio source separation, which also in-
volves estimation and inference of source signals from mixture
observations. It is also useful for many high-level tasks in
MIR [4] such as structural segmentation, cover-song detection
and assessment of music similarity, since these tasks are
much easier to address once the musical notes are known.
Thus, AMT provides the main link between the fields of
music signal processing and symbolic music processing (i.e.,
processing of music notation and music language modeling).
The integration of the two aforementioned fields through AMT
will be discussed in Section IV.
Given the potential impact of AMT, the problem has also
attracted commercial interest in addition to academic research.
While it is outside the scope of the paper to provide a com-
prehensive list of commercial AMT software, commonly used
software includes Melodyne2, AudioScore3, ScoreCloud4, An-
themScore5, and Transcribe!6. It is worth noting that AMT
papers in the literature have refrained from making explicit
comparisons with commercially available music transcription
software, possibly due to different scopes and target applica-
tions between commercial and academic tools.
B. Analogies to Other Fields
AMT has close relations with other signal processing prob-
lems. With respect to the field of speech processing, AMT is
widely considered to be the musical equivalent of Automatic
Speech Recognition (ASR), in the sense that both tasks involve
converting acoustic signals to symbolic sequences. Like the
Time (seconds)
MIDI pitch Frequency (Hz) Amplitude
12 3 4 5 6
12 3 4 5 6
12 3 4 5 6
Figure 1. Data represented in an AMT system. (a) Input waveform, (b) Internal time-frequency representation, (c) Output piano-roll representation, (d)
Output music score, with notes A and D marked in gray circles. The example corresponds to the first 6 seconds of W. A. Mozart’s Piano Sonata No. 13, 3rd
movement (taken from the MAPS database).
cocktail party problem in speech, music usually involves mul-
tiple simultaneous voices, but unlike speech, these voices are
highly correlated in time and in frequency (see Challenges 2
and 3 in Section I-C). In addition, both AMT and ASR systems
benefit from language modeling components that are combined
with acoustic components in order to produce plausible results.
Thus, there are also clear links between AMT and the wider
field of natural language processing (NLP), with music having
its own grammatical rules or statistical regularities, in a similar
way to natural language [5]. The use of language models for
AMT is detailed in Section IV.
Within the emerging field of sound scene analysis, there is
a direct analogy between AMT and Sound Event Detection
(SED) [6], in particular with polyphonic SED which involves
detecting and classifying multiple overlapping events from
audio. While everyday and natural sounds do not exhibit the
same degree of temporal regularity and inter-source frequency
dependence as found in music signals, there are close interac-
tions between the two problems in terms of the methodologies
used, as observed in the literature [6].
Further, AMT is related to image processing and computer
vision, as musical objects such as notes can be recognized
as two-dimensional patterns in time-frequency representations.
Compared with image processing and computer vision, where
occlusion is a common issue, AMT systems are often affected
by musical objects occupying the same time-frequency regions
(this is detailed in Section I-C).
C. Key Challenges
Compared to other problems in the music signal processing
field or the wider signal processing discipline, there are several
factors that make AMT particularly challenging:
1) Polyphonic music contains a mixture of multiple simul-
taneous sources (e.g., instruments, vocals) with different
pitch, loudness and timbre (sound quality), with each
source producing one or more musical voices. Inferring
musical attributes (e.g., pitch) from the mixture signal
is an extremely under-determined problem.
2) Overlapping sound events often exhibit harmonic re-
lations with each other; for any consonant musical
interval, the fundamental frequencies form small integer
ratios, so that their harmonics overlap in frequency,
making the separation of the voices even more difficult.
Taking a C major chord as an example, the fundamental
frequency ratio of its three notes C:E:G is 4:5:6, and the
percentage of harmonic positions that are overlapped by
the other notes are 46.7%, 33.3% and 60% for C, E and
G, respectively.
3) The timing of musical voices is governed by the regular
metrical structure of the music. In particular, musicians
pay close attention to the synchronization of onsets
and offsets between different voices, which violates the
common assumption of statistical independence between
sources which otherwise facilitates separation.
4) The annotation of ground-truth transcriptions for poly-
phonic music is very time consuming and requires high
expertise. The lack of such annotations has limited
the use of powerful supervised learning techniques to
specific AMT sub-problems such as piano transcription,
where the annotation can be automated due to certain
piano models that can automatically capture perfor-
mance data. An approach to circumvent this problem
was proposed in [7], however, it requires professional
music performers and thorough score pre- and post-
processing. We note that sheet music does not generally
provide good ground-truth annotations for AMT; it is
not time-aligned to the audio signal, nor does it usually
provide an accurate representation of a performance.
Even when accurate transcriptions exist, it is not trivial
to identify corresponding pairs of audio files and musical
scores, because of the multitude of versions of any given
musical work that are available from music distributors.
At best, musical scores can be viewed as weak labels.
The above key challenges are often not fully addressed in
current AMT systems, leading to common issues in the AMT
outputs, such as octave errors, semitone errors, missed notes
(in particular in the presence of dense chords), extra notes
(often manifested as harmonic errors in the presence of unseen
timbres), merged or fragmented notes, incorrect onsets/offsets,
or mis-assigned streams [1], [2]. The remainder of the paper
will focus on ways to address the above challenges, as well
as discussion of additional open problems for the creation of
robust AMT systems.
In the past four decades, many approaches have been
developed for AMT for polyphonic music. While the end goal
of AMT is to convert an acoustic music recording to some
form of music notation, most approaches were designed to
achieve a certain intermediate goal. Depending on the level
of abstraction and the structures that need to be modeled
for achieving such goals, AMT approaches can be generally
organized into four categories: frame-level, note-level, stream-
level and notation-level.
Frame-level transcription, or Multi-Pitch Estimation (MPE),
is the estimation of the number and pitch of notes that are
simultaneously present in each time frame (on the order of
10 ms). This is usually performed in each frame indepen-
dently, although contextual information is sometimes consid-
Figure 2. Examples of frame-level, note-level and stream-level transcriptions,
produced by running methods proposed in [8], [9] and [10], respectively, of
the first phrase of J. S. Bach’s chorale “Ach Gott und Herr” from the Bach10
dataset. All three levels are parametric descriptions of the music performance.
ered through filtering frame-level pitch estimates in a post-
processing stage. Fig. 2(top) shows an example of a frame-
level transcription, where each black dot is a pitch estimate.
Methods in this category do not form the concept of musical
notes and rarely model any high-level musical structures. A
large portion of existing AMT approaches operate at this level.
Recent approaches include traditional signal processing meth-
ods [11], [12], probabilistic modeling [8], Bayesian approaches
[13], non-negative matrix factorization (NMF) [14], [15], [16],
[17], and neural networks [18], [19]. All of these methods
have pros and cons and the research has not converged to
a single approach. For example, traditional signal processing
methods are simple and fast and generalize better to different
instruments, while deep neural network methods generally
achieve higher accuracy on specific instruments (e.g., piano).
Bayesian approaches provide a comprehensive modeling of
the sound generation process, however models can be very
complex and slow. Readers interested in a comparison of the
performance of different approaches are referred to the Mul-
tiple Fundamental Frequency Estimation & Tracking task of
the annual Music Information Retrieval Evaluation eXchange
(MIREX) 7. However, readers are reminded that evaluation
results may be biased by the limitations of datasets and
evaluation metrics (see Sections I-C and IV-G).
Note-level transcription, or note tracking, is one level higher
than MPE, in terms of the richness of structures of the
estimates. It not only estimates the pitches in each time frame,
but also connects pitch estimates over time into notes. In
the AMT literature, a musical note is often characterized by
three elements: pitch, onset time, and offset time [1]. As
note offsets can be ambiguous, they are sometimes neglected
in the evaluation of note tracking approaches, and as such,
some note tracking approaches only estimate pitch and onset
times of notes. Fig. 2(middle) shows an example of a note-
level transcription, where each note is shown as a red circle
(onset) followed by a black line (pitch contour). Many note
tracking approaches form notes by post-processing MPE out-
puts (i.e., pitch estimates in individual frames). Techniques
that have been used in this context include median filtering
[12], Hidden Markov Models (HMMs) [20], and neural net-
works [5]. This post-processing is often performed for each
MIDI pitch independently without considering the interactions
among simultaneous notes. This often leads to spurious or
missing notes that share harmonics with correctly estimated
notes. Some approaches have been proposed to consider note
interactions through a spectral likelihood model [9] or a music
language model [5], [18] (see Section IV-A). Another subset
of approaches estimate notes directly from the audio signal
instead of building upon MPE outputs. Some approaches first
detect onsets and then estimate pitches within each inter-onset
interval [21], while others estimate pitch, onset and sometimes
offset in the same framework [22], [23], [24].
Stream-level transcription, also called Multi-Pitch Stream-
ing (MPS), targets grouping estimated pitches or notes into
streams, where each stream typically corresponds to one in-
strument or musical voice, and is closely related to instrument
source separation. Fig. 2(bottom) shows an example of a
stream-level transcription, where pitch streams of different
instruments have different colors. Compared to note-level
transcription, the pitch contour of each stream is much longer
than a single note and contains multiple discontinuities that are
caused by silence, non-pitched sounds and abrupt frequency
changes. Therefore, techniques that are often used in note-level
transcription are generally not sufficient to group pitches into
a long and discontinuous contour. One important cue for MPS
that is not explored in MPE and note tracking is timbre: notes
of the same stream (source) generally show similar timbral
characteristics compared to those in different streams. There-
fore, stream-level transcription is also called timbre tracking
or instrument tracking in the literature. Existing works at this
level are few, with [16], [10], [25] as examples.
From frame-level to note-level to stream-level, the transcrip-
tion task becomes more complex as more musical structures
and cues need to be modeled. However, the transcription
outputs at these three levels are all parametric transcriptions,
which are parametric descriptions of the audio content. The
MIDI piano roll shown in Fig. 1(c) is a good example of such
a transcription. It is indeed an abstraction of music audio,
however, it has not yet reached the level of abstraction of
music notation: time is still measured in the unit of seconds
instead of beats; pitch is measured in MIDI numbers instead
of spelled note names that are compatible with the key (e.g.,
C]vs D[); and the concepts of beat, bar, meter, key, harmony,
and stream are lacking.
Notation-level transcription aims to transcribe the music au-
dio into a human readable musical score, such as the staff no-
tation widely used in Western classical music. Transcription at
this level requires deeper understanding of musical structures,
including harmonic, rhythmic and stream structures. Harmonic
structures such as keys and chords influence the note spelling
of each MIDI pitch; rhythmic structures such as beats and bars
help to quantize the lengths of notes; and stream structures
aid the assignment of notes to different staffs. There has
been some work on the estimation of musical structures from
audio or MIDI representations of a performance. For example,
methods for pitch spelling [26], timing quantization [27], and
voice separation [28] from performed MIDI files have been
proposed. However, little work has been done on integrating
these structures into a complete music notation transcription,
especially for polyphonic music. Several software packages,
including Finale, GarageBand and MuseScore, provide the
functionality of converting a MIDI file into music notation,
however, the results are often not satisfying and it is not clear
what musical structures have been estimated and integrated
during the transcription process. Cogliati et al. [29] proposed
a method to convert a MIDI performance into music notation,
with a systematic comparison of the transcription performance
with the above-mentioned software. In terms of audio-to-
notation transcription, a proof-of-concept work using end-to-
end neural networks was proposed by Carvalho and Smaragdis
[30] to directly map music audio into music notation without
explicitly modeling musical structures.
While there is a wide range of applicable methods, auto-
matic music transcription has been dominated during the last
decade by two algorithmic families: Non-Negative Matrix Fac-
torization (NMF) and Neural Networks (NNs). Both families
have been used for a variety of tasks, from speech and image
processing to recommender systems and natural language
processing. Despite this wide applicability, both approaches
offer a range of properties that make them particularly suitable
for modeling music recordings at the note level.
A. Non-negative Matrix Factorization for AMT
The basic idea behind NMF and its variants is to rep-
resent a given non-negative time-frequency representation
0, e.g., a magnitude spectrogram, as a product of
two non-negative matrices: a dictionary DRM×K
0and an
activation matrix ARK×N
0, see Fig. 3. Computationally,
the goal is to minimize a distance (or divergence) between
Vand DA with respect to Dand A. As a straightforward
approach to solving this minimization problem, multiplicative
update rules have been central to the success of NMF. For
example, the generalized Kullback-Leibler divergence between
Vand DA is non-increasing under the following updates and
Time (seconds)
MIDI pitch
MIDI pitch
Frequency (Hz)
Time (seconds)
Frequency (Hz)
Frequency (Hz)
40 60 80
12 3 4 5 61 2 34 5 6
1 2 3 4 56
Figure 3. NMF example, using the same audio recording as Fig. 1. (a) Input spectrogram V, (b) Approximated spectrogram DA, (c) Dictionary D
(pre-extracted), (d) Activation matrix A.
guarantees the non-negativity of both Dand Aas long as both
are initialized with positive real values [31]:
DA )
D>Jand DD(V
DA )A>
where the operator denotes point-wise multiplication, J
RM×Ndenotes the matrix of ones, and the division is point-
wise. Intuitively, the update rules can be derived by choosing
a specific step-size in a gradient (or rather coordinate) descent
based minimization of the divergence [31].
In an AMT context, both unknown matrices have an intu-
itive interpretation: the n-th column of V, i.e. the spectrum at
time point n, is modeled in NMF as a linear combination of
the Kcolumns of D, and the corresponding Kcoefficients
are given by the n-th column of A. Given this point of view,
each column of Dis often referred to as a (spectral) template
and usually represents the expected spectral energy distribution
associated with a specific note played on a specific instrument.
For each template, the corresponding row in Ais referred to
as the associated activation and encodes when and how in-
tensely that note is played over time. Given the non-negativity
constraints, NMF yields a purely constructive representation
in the sense that spectral energy modeled by one template
cannot be cancelled by another – this property is often seen
as instrumental in identifying a parts-based and interpretable
representation of the input [31].
In Fig. 3, an NMF-based decomposition is illustrated. The
magnitude spectrogram Vshown in Fig. 3(a) is modeled as
a product of the dictionary Dand activation matrix Ashown
in Fig. 3(c) and (d), respectively. The product DA is given
in Fig. 3(b). In this case, the templates correspond to indi-
vidual pitches, with clearly visible fundamental frequencies
and harmonics. Additionally, comparing Awith the piano
Figure 4. Inharmonicity: Spectrum of a C]1 note played on a piano. The
stiffness of strings causes partials to be shifted from perfect integer multiples
of the fundamental frequency (shown as vertical dotted lines); here the 23rd
partial is at the position where the 24th harmonic would be expected. Note
that the fundamental frequency of 34.65Hz is missing as piano soundboards
typically do not resonate for modes with a frequency smaller than 50Hz.
roll representation shown in Fig. 1(c) indicates the correlation
between NMF activations and the underlying musical score.
While Fig. 3illustrates the principles behind NMF, it also
indicates why AMT is difficult – indeed, a regular NMF
decomposition would rarely look as clean as in Fig. 3. Com-
pared to speech analysis, sound objects in music are highly
correlated. For example, even in a simple piece as shown
in Fig. 1, most pairs of simultaneous notes are separated
by musically consonant intervals, which acoustically means
that many of their partials overlap (e.g., the A and D notes
around 4 seconds, marked with gray circles in Fig. 1(d), share
a high number of partials). In this case, it can be difficult
to disentangle how much energy belongs to which note.
The task is further complicated by the fact that the spectro-
temporal properties of notes vary considerably between differ-
ent pitches, playing styles, dynamics and recording conditions.
Further, stiffness properties of strings affect the travel speed
of transverse waves based on their frequency – as a result,
Frequency in Hz
Figure 5. Harmonic NMF [15]: Each NMF template (right hand side) is
represented as a linear combination of fixed narrow-band sub-templates. The
resulting template is constrained to represent harmonic sounds by construction.
the partials of instruments such as the piano are not found at
perfect integer multiples of the fundamental frequency. Due
to this property called inharmonicity, the positions of partials
differ between individual pianos (see Fig. 4).
To address these challenges, the basic NMF model has been
extended by encouraging additional structure in the dictionary
and the activations. For example, an important principle is
to enforce sparsity in Ato obtain a solution dominated by
few but substantial activations – the success of sparsity paved
the way for a whole range of sparse coding approaches, in
which the dictionary size Kcan exceed the input dimension
Mconsiderably [32]. Other extensions focus on the dictionary
design. In the case of supervised NMF, the dictionary is
pre-computed and fixed using additionally available training
material. For example, given Krecordings each containing
only a single note, the dictionary shown in Fig. 3(b) was
constructed by extracting one template from each recording –
this way, the templates are guaranteed to be free of interference
from other notes and also have a clear interpretation. As
another example, Fig. 5illustrates an extension in which each
NMF template is represented as a linear combination of fixed
narrow-band sub-templates [15], which enforces a harmonic
structure for all NMF templates – this way, a dictionary can be
adapted to the recording to be transcribed, while maintaining
its clean, interpretable structure.
In shift-invariant dictionaries a single template can be used
to represent a range of different fundamental frequencies. In
particular, using a logarithmic frequency axis, the distances
between individual partials of a harmonic sound are fixed and
thus shifting a template in frequency allows modeling sounds
of varying pitch. Sharing parameters between different pitches
in this way has turned out to be effective towards increasing
model capacity (see e.g., [16], [17]). Further, spectro-temporal
dictionaries alleviate a specific weakness of NMF models: in
NMF it is difficult to express that notes often have a specific
temporal evolution – e.g., the beginning of a note (or attack
phase) might have entirely different spectral properties than
the central part (decay phase). Such relationships are modeled
in spectro-temporal dictionaries using a Markov process which
governs the sequencing of templates across frames, so that
different subsets of templates can be used for the attack and
the decay parts, respectively [16], [23].
B. Neural Networks for AMT
As for many tasks relating to pattern recognition, neural
networks (NNs) have had a considerable impact in recent
years on the problem of music transcription and on music
signal processing in general. NNs are able to learn a non-
linear function (or a composition of functions) from input
to output via an optimization algorithm such as stochastic
gradient descent [33]. Compared to other fields including
image processing, progress on NNs for music transcription
has been slower and we will discuss a few of the underlying
reasons below.
One of the earliest approaches based on neural networks
was Marolt’s Sonic system [21]. A central component in
this approach was the use of time-delay (TD) networks,
which resemble convolutional networks in the time direction
[33], and were employed to analyse the output of adaptive
oscillators, in order to track and group partials in the output
of a gammatone filterbank. Although it was initially published
in 2001, the approach remains competitive and still appears in
comparisons in more recent publications [23].
In the context of the more recent revival of neural networks,
a first successful system was presented by Böck and Schedl
[34]. One of the core ideas was to use two spectrograms
as input to enable the network to exploit both a high time
accuracy (when estimating the note onset position) and a
high frequency resolution (when disentangling notes in the
lower frequency range). This input is processed using one
(or more) Long Short-Term Memory (LSTM) layers [33].
The potential benefit of using LSTM layers is two-fold. First,
the spectral properties of a note evolve across input frames
and LSTM networks have the capability to compactly model
such sequences. Second, medium and long range dependencies
between notes can potentially be captured: for example, based
on a popular chord sequence, after hearing C and G major
chords followed by A minor, a likely successor is an F
major chord. An investigation of whether such long-range
dependencies are indeed modeled, however, was not in scope.
Sigtia et al. [18] focus on long-range dependencies in
music by combining an acoustic front-end with a symbolic-
level module resembling a language model as used in speech
processing. Using information obtained from MIDI files, a
recurrent network is trained to predict the active notes in
the next time frame given the past. This approach needs to
learn and represent a very large joint probability distribution,
i.e., a probability for every possible combination of active and
inactive notes across time – note that even in a single frame
there are 288 possible combinations of notes on a piano. To
render the problem of modeling such an enormous probability
space tractable, the approach employs a specific neural net-
work architecture (NADE), which represents a large joint as a
long product of conditional probabilities – an approach quite
similar to the idea popularized recently by the well-known
WaveNet architecture. Despite the use of a dedicated music
language model, which was trained on relatively large MIDI-
based datasets, only modest improvements over an HMM
baseline could be observed and thus the question remains open
to which degree long-range dependencies are indeed captured.
To further disentangle the influence of the acoustic front-end
from the language model on potential improvement in perfor-
mance, Kelz et al. [19] focus on the acoustic modeling and
report on the results of a larger scale hyperparameter search
Conv Stack
FC Sigmoid
Conv Stack
FC Sigmoid
Log Mel
Figure 6. Google Brain’s Onset and Frames Network: The input is processed
by a first network detecting note onsets. The result is used as side information
for a second network focused on estimating note lengths (adapted from [24]).
Bi LSTM refers to bi-directional LSTM layers; FC Sigmoid refers to a fully
connected sigmoid layer; Conv Stack refers to a series of convolutional layers.
and describe the influence of individual system components.
Trained using this careful and extensive procedure the resulting
model outperforms existing models by a reasonable margin. In
other words, while in speech processing, language models have
led to a drastic improvement in performance, the same effect
is still to be demonstrated in an AMT system – a challenge
we will discuss in more detail below.
The development of neural network based AMT approaches
continues: the current state of the art method for general
purpose piano transcription was proposed by Google Brain
[24]. Combining and extending ideas from existing methods,
this approach combines two networks (Fig. 6): one network is
used to detect note onsets and its output is used to inform a
second network, which focuses on detecting note lengths. This
can be interpreted from a probabilistic point of view: note
onsets are rare events compared to frame-wise note activity
detections – the split into two network branches can thus
be interpreted as splitting the representation of a relatively
complex joint probability distribution over onsets and frame
activity into a probability over onsets and a probability over
frame activities, conditioned on the onset distribution. Since
the temporal dynamics of onsets and frame activities are quite
different, this can lead to improved learning behavior for the
entire network when trained jointly.
C. A Comparison of NMF and Neural Network Models
Given the popularity of NMF and neural network based
methods for automatic music transcription, it is interesting
to discuss their differences. In particular, neglecting the non-
negativity constraints, NMF is a linear, generative model.
Given that NMF-based methods are increasingly replaced by
NN-based ones, the question arises in which way linearity
could be a limitation for an AMT model.
To look into this, assume we are given an NMF dictio-
nary with two spectral templates for each musical pitch. To
represent an observed spectrum of a single pitch C4, we can
linearly combine the two templates associated with C4. The
set (or manifold) of valid spectra for C4 notes, however, is
complex and thus in most cases our linear interpolation will
not correspond to a real-world recording of a C4. We could
increase the number of templates such that their interpolation
could potentially get closer to a real C4 – however, the
number of invalid spectra we can represent increases much
more quickly compared to the number of valid spectra. Deep
networks have shown considerable potential in recent years to
(implicitly) represent such complex manifolds in a robust and
comparatively efficient way [33]. An additional benefit over
generative models such as NMF is that neural networks can be
trained in an end-to-end fashion, i.e., note detections can be a
direct output of a network without the need for additional post-
processing of model parameters (such as NMF activations).
Yet, despite these quite principled limitations, NMF-based
methods remain competitive or even exceed results achieved
using neural networks. Currently, there are two main chal-
lenges for neural network-based approaches. First, there are
only few, relatively small annotated datasets available, and
these are often subject to severe biases [7]. The largest publicly
available dataset [11] contains several hours of piano music
– however, all recorded on only seven different (synthesizer-
based and real) pianos. While typical data augmentation
strategies such as pitch shifting or simulating different room
acoustics might mitigate some of the effects, there is still a
considerable risk that a network overfits the acoustic properties
of these specific instruments. For many types of instruments,
even small datasets are not available. Other biases include
musical style as well as the distribution over central musical
concepts, such as key, harmony, tempo and rhythm.
A second considerable challenge is the adaptability to
new acoustic conditions. Providing just a few examples of
isolated notes of the instrument to be transcribed, considerable
improvements are observed in the performance of NMF based
models. There is currently no corresponding equally effective
mechanism to re-train or adapt a neural network based AMT
system on a few seconds of audio – thus the error rate for non-
adapted networks can be an order of magnitude higher than
that of an adapted NMF system [23], [24]. Overall, as both
of these challenges cannot easily be overcome, NMF-based
methods are likely to remain relevant in specific use cases.
In Fig. 7, we qualitatively illustrate some differences in the
behavior of systems based on supervised NMF and neural
networks. Both systems were specifically trained for tran-
scribing piano recordings and we expose the approaches to
a recording of an organ. Like the piano, the organ is played
with a keyboard but its acoustic properties are quite different:
the harmonics of the organ are rich in energy and cover the
entire spectrum, the energy of notes does not decay over time
and onsets are less pronounced. With this experiment, we
want to find out how gracefully the systems fail when they
encounter a sound that is outside the piano-sound manifold but
still musically valid. Comparing the NMF output in Fig. 7(a)
and the NN output in Fig. 7(b) with the ground truth, we
find that both methods detect additional notes (shown in red),
mostly at octaves above and below the correct fundamental.
Time (seconds)
MIDI pitch
Time (seconds)
MIDI pitch
1 2 3 4 5 6
12 3 4 5 6
Figure 7. Piano-roll representations of the first 6 seconds of a recording of
a Bach piece (BWV 582) for organ. Black color corresponds to correctly
detected pitches, red to false positives, and blue to false negatives. (a) Output
of NMF model trained on piano templates. (b) Output of the piano music-
trained neural network model of [24].
Given the rich energy distribution, this behavior is expected.
While we use a simple baseline model for NMF and thus some
errors could be attributed to that choice, the neural network
fails more gracefully: fewer octave errors and fewer spurious
short note detections are observed (yet in terms of recall the
NMF-based approach identifies additional correct notes). It is
difficult to argue why the acoustic model within the network
should be better prepared to such a situation. However, the
results suggest that the network learned something additional:
the LSTM layers as used in the network (compare Fig. 6) seem
to have learned how typical piano notes evolve in time and thus
most note lengths look reasonable and less spurious. Similarly,
the bandwidth in which octave errors occur is narrower for
the neural network, which could potentially indicate that the
network models the likelihood of co-occurring notes or, in
other words, a simple music language model, which leads us
to our discussion of important remaining challenges in AMT.
A. Music Language Models
As outlined in Section I-B, AMT is closely related to
automatic speech recognition (ASR). In the same way that
a typical ASR system consists of an acoustic component and
a language component, an AMT system can model both the
acoustic sequences and also the underlying sequence of notes
and other music cues over time. AMT systems have thus
incorporated music language models (MLMs) for modeling
sequences of notes in a polyphonic context, with the aim
of improving transcription performance. The capabilities of
deep learning methods towards modeling high-dimensional
sequences have recently made polyphonic music sequence pre-
diction possible. Boulanger-Lewandowski et al. [5] combined
a restricted Bolzmann machine (RBM) with an RNN for poly-
phonic music prediction, which was used to post-process the
acoustic output of an AMT system. Sigtia et al. [18] also used
the aforementioned RNN-RBM as an MLM, and combined
the acoustic and language predictions using a probabilistic
graphical model. While these initial works showed promising
results, there are several directions for future research in
MLMs; these include creating unified acoustic and language
models (as opposed to using MLMs as post-processing steps)
and modeling other musical cues, such as chords, key and
meter (as opposed to simply modeling note sequences).
B. Score-Informed Transcription
If a known piece is performed, the musical score provides a
strong prior for the transcription. In many cases, there are dis-
crepancies between the score and a given music performance,
which may be due to a specific interpretation by a performer,
or due to performance mistakes. For applications such as
music education, it is useful to identify such discrepancies, by
incorporating the musical score as additional prior information
to simplify the transcription process (score-informed music
transcription [35]). Typically, systems for score-informed mu-
sic transcription use a score-to-audio alignment method as a
pre-processing step, in order to align the music score with
the input music audio prior to performing transcription, e.g.
[35]. While specific instances of score-informed transcription
systems have been developed for certain instruments (piano,
violin), the problem is still relatively unexplored, as is the
related and more challenging problem of lead sheet-informed
transcription and the eventual integration of these methods
towards the development of automatic music tutoring systems.
C. Context-Specific Transcription
While the creation of a “blind” multi-instrument AMT
system without specific knowledge of the music style, in-
struments and recording conditions is yet to be achieved,
considerable progress has been reported on the problem of
context-specific transcription, where prior knowledge of the
sound of the specific instrument model or manufacturer and the
recording environment is available. For context-specific piano
transcription, multi-pitch detection accuracy can exceed 90%
[23], [22], making such systems appropriate for user-facing
applications. Open work in this topic includes the creation of
context-specific AMT systems for multiple instruments.
D. Non-Western Music
As might be evident by surveying the AMT literature, the
vast majority of approaches target only Western (or Euroge-
netic) music. This allows several assumptions, regarding both
the instruments used and also the way that music is represented
and produced (typical assumptions include: octaves containing
12 equally-spaced pitches; two modes, major and minor; a
standard tuning frequency of A4 = 440 Hz). However, these
assumptions do not hold true for other music styles from
around the world, where for instance an octave is often
divided into microtones (e.g., Arabic music theory is based
on quartertones), or on the existence of modes that are not
used in Western music (e.g., classical Indian music recognizes
hundreds of modes, called ragas). Therefore, automatically
transcribing non-Western music still remains an open problem
with several challenges, including the design of appropriate
signal and music notation representations while avoiding a so-
called Western bias [36]. Another major issue is the lack of
annotated datasets for non-Western music, rendering the ap-
plication of data-intensive machine learning methods difficult.
E. Expressive Pitch and Timing
Western notation conceptualizes music as sequences of
unchanging pitches being maintained for regular durations, and
has little scope for representing expressive use of microtonality
and microtiming, nor for detailed recording of timbre and
dynamics. Research on automatic transcription has followed
this narrow view, describing notes in terms of discrete pitches
plus onset and offset times. For example, no suitable notation
exists for performed singing, the most universal form of music-
making. Likewise for other instruments without fixed pitch
or with other expressive techniques, better representations are
required. These richer representations can then be reduced
to Western score notation, if required, by modeling musical
knowledge and stylistic conventions.
F. Percussion and Unpitched Sounds
An active problem in the music signal processing literature
is that of detecting and classifying non-pitched sounds in
music signals [1, Ch. 5]. In most cases this is expressed as
the problem of drum transcription, since the vast majority
of contemporary music contains mixtures of pitched sounds
and unpitched sounds produced by a drum kit. Drum kit
components typically include the bass drum, snare drum, hi-
hat, cymbals and toms. The problem in this case is to detect
and classify percussive sounds into one of the aforementioned
sound classes. Elements of the drum transcription problem that
make it particularly challenging are the concurrent presence of
several harmonic, inharmonic and non-harmonic sounds in the
music signal, as well as the requirement of an increased tem-
poral resolution for drum transcription systems compared to
typical multi-pitch detection systems. Approaches for pitched
instrument transcription and drum transcription have largely
been developed independently, and the creation of a robust
music transcription system that supports both pitched and
unpitched sounds still remains an open problem.
G. Evaluation Metrics
Most AMT approaches are evaluated using the set of metrics
proposed for the MIREX Multiple-F0 Estimation and Note
Tracking public evaluation tasks8. Three types of metrics are
included: frame-based,note-based and stream-based, mirror-
ing the frame-level, note-level, and stream-level transcription
categories presented in Sec. III. While the above sets of
metrics all have their merits, it could be argued that they do
not correspond with human perception of music transcription
accuracy, where e.g., an extra note might be considered as a
more severe error than a missed note, or where out-of-key note
errors might be penalized more compared with in-key ones.
Therefore, the creation of perceptually relevant evaluation
metrics for AMT, as well as the creation of evaluation metrics
for notation-level transcription, remain open problems.
Automatic music transcription has remained an active area
of research in the fields of music signal processing and music
information retrieval for several decades, with several potential
benefits in other areas and fields extending beyond the remit
of music. As outlined in this paper, there remain several
challenges to be addressed in order to fully address this
problem: these include key challenges as described in Section
I-C on modeling music signals and on the availability of data,
challenges with respect to the limitations of state-of-the-art
methodologies as described in Section III-C, and finally on ex-
tensions beyond the current remit of existing tasks as presented
in Section IV. We believe that addressing these challenges will
lead towards the creation of a “complete” music transcription
system and towards unlocking the full potential of music signal
processing technologies. Supplementary audio material related
to this paper can be found in the companion website9.
[1] A. Klapuri and M. Davy, Eds., Signal Processing Methods for Music
Transcription. New York: Springer, 2006.
[2] E. Benetos, S. Dixon, D. Giannoulis, H. Kirchhoff, and A. Klapuri, “Au-
tomatic music transcription: challenges and future directions,” Journal
of Intelligent Information Systems, vol. 41, no. 3, pp. 407–434, Dec.
[3] M. Müller, D. P. Ellis, A. Klapuri, and G. Richard, “Signal processing for
music analysis,” IEEE Journal of Selected Topics in Signal Processing,
vol. 5, no. 6, pp. 1088–1110, Oct. 2011.
[4] M. Schedl, E. Gómez, and J. Urbano, “Music information retrieval:
Recent developments and applications,Foundations and Trends in
Information Retrieval, vol. 8, pp. 127–261, 2014.
[5] N. Boulanger-Lewandowski, Y. Bengio, and P. Vincent, “Modeling
temporal dependencies in high-dimensional sequences: Application to
polyphonic music generation and transcription,” in Proc. International
Conference on Machine Learning (ICML), 2012.
[6] T. Virtanen, M. D. Plumbley, and D. P. W. Ellis, Eds., Computational
Analysis of Sound Scenes and Events. Springer, 2018.
[7] L. Su and Y.-H. Yang, “Escaping from the abyss of manual annotation:
New methodology of building polyphonic datasets for automatic music
transcription,” in Proc. International Symposium on Computer Music
Multidisciplinary Research (CMMR), 2015, pp. 309–321.
[8] Z. Duan, B. Pardo, and C. Zhang, “Multiple fundamental frequency
estimation by modeling spectral peaks and non-peak regions,” IEEE
Transactions on Audio, Speech, and Language Processing, vol. 18, no. 8,
pp. 2121–2133, 2010.
[9] Z. Duan and D. Temperley, “Note-level music transcription by maximum
likelihood sampling.” in ISMIR, 2014, pp. 181–186.
[10] Z. Duan, J. Han, and B. Pardo, “Multi-pitch streaming of harmonic
sound mixtures,” IEEE/ACM Transactions on Audio, Speech, and Lan-
guage Processing, vol. 22, no. 1, pp. 138–150, Jan 2014.
[11] V. Emiya, R. Badeau, and B. David, “Multipitch estimation of piano
sounds using a new probabilistic spectral smoothness principle,” IEEE
Transactions on Audio, Speech, and Language Processing, vol. 18, no. 6,
pp. 1643–1654, 2010.
[12] L. Su and Y.-H. Yang, “Combining spectral and temporal representations
for multipitch estimation of polyphonic music,” IEEE/ACM Transactions
on Audio, Speech, and Language Processing, vol. 23, no. 10, pp. 1600–
1612, Oct 2015.
[13] P. H. Peeling, A. T. Cemgil, and S. J. Godsill, “Generative spectrogram
factorization models for polyphonic piano transcription,” IEEE Trans-
actions on Audio, Speech, and Language Processing, vol. 18, no. 3, pp.
519–527, March 2010.
9 overview/
[14] P. Smaragdis and J. C. Brown, “Non-negative matrix factorization for
polyphonic music transcription,” in Proc. IEEE Workshop on Applica-
tions of Signal Processing to Audio and Acoustics, 2003, pp. 177–180.
[15] E. Vincent, N. Bertin, and R. Badeau, “Adaptive harmonic spectral
decomposition for multiple pitch estimation,” IEEE Transactions on
Audio, Speech, and Language Processing, vol. 18, no. 3, pp. 528–537,
[16] E. Benetos and S. Dixon, “Multiple-instrument polyphonic music tran-
scription using a temporally-constrained shift-invariant model,Journal
of the Acoustical Society of America, vol. 133, no. 3, pp. 1727–1741,
March 2013.
[17] B. Fuentes, R. Badeau, and G. Richard, “Harmonic adaptive latent
component analysis of audio and application to music transcription,”
IEEE Transactions on Audio, Speech, and Language Processing, vol. 21,
no. 9, pp. 1854–1866, Sept 2013.
[18] S. Sigtia, E. Benetos, and S. Dixon, “An end-to-end neural network
for polyphonic piano music transcription,” IEEE/ACM Transactions on
Audio, Speech, and Language Processing, vol. 24, no. 5, pp. 927–939,
May 2016.
[19] R. Kelz, M. Dorfer, F. Korzeniowski, S. Böck, A. Arzt, and G. Widmer,
“On the potential of simple framewise approaches to piano transcrip-
tion,” in Proc. International Society for Music Information Retrieval
Conference, 2016, pp. 475–481.
[20] J. Nam, J. Ngiam, H. Lee, and M. Slaney, “A classification-based
polyphonic piano transcription approach using learned feature represen-
tations,” in ISMIR, 2011, pp. 175–180.
[21] M. Marolt, “A connectionist approach to automatic transcription of
polyphonic piano music,” IEEE Transactions on Multimedia, vol. 6,
no. 3, pp. 439–449, 2004.
[22] A. Cogliati, Z. Duan, and B. Wohlberg, “Context-dependent piano music
transcription with convolutional sparse coding,IEEE/ACM Transactions
on Audio, Speech, and Language Processing, vol. 24, no. 12, pp. 2218–
2230, Dec 2016.
[23] S. Ewert and M. B. Sandler, “Piano transcription in the studio using an
extensible alternating directions framework,IEEE/ACM Transactions
on Audio, Speech, and Language Processing, vol. 24, no. 11, pp. 1983–
1997, Nov 2016.
[24] C. Hawthorne, E. Elsen, J. Song, A. Roberts, I. Simon, C. Raffel,
J. Engel, S. Oore, and D. Eck, “Onsets and frames: Dual-objective piano
transcription,” in Proc. International Society for Music Information
Retrieval Conference, 2018.
[25] V. Arora and L. Behera, “Multiple F0 estimation and source clustering
of polyphonic music audio using PLCA and HMRFs,” IEEE/ACM
Transactions on Audio, Speech and Language Processing (TASLP),
vol. 23, no. 2, pp. 278–287, 2015.
[26] E. Cambouropoulos, “Pitch spelling: A computational model,” Music
Perception, vol. 20, no. 4, pp. 411–429, 2003.
[27] H. Grohganz, M. Clausen, and M. Mueller, “Estimating musical time
information from performed MIDI files,” in Proc. International Society
for Music Information Retrieval Conference, 2014.
[28] I. Karydis, A. Nanopoulos, A. Papadopoulos, E. Cambouropoulos, and
Y. Manolopoulos, “Horizontal and vertical integration/segregation in
auditory streaming: a voice separation algorithm for symbolic musical
data,” in Proc. Sound and Music Computing Conference (SMC), 2007.
[29] A. Cogliati, D. Temperley, and Z. Duan, “Transcribing human piano
performances into music notation,” in Proc. International Society for
Music Information Retrieval Conference, 2016, pp. 758–764.
[30] R. G. C. Carvalho and P. Smaragdis, “Towards end-to-end polyphonic
music transcription: Transforming music audio directly to a score,” in
2017 IEEE Workshop on Applications of Signal Processing to Audio and
Acoustics, Oct 2017, pp. 151–155.
[31] D. D. Lee and H. S. Seung, “Algorithms for non-negative matrix
factorization,” in Advances in neural information processing systems
(NIPS), 2001, pp. 556–562.
[32] S. A. Abdallah and M. D. Plumbley, “Unsupervised analysis of poly-
phonic music by sparse coding,” IEEE Transactions on neural Networks,
vol. 17, no. 1, pp. 179–196, 2006.
[33] I. Goodfellow, Y. Bengio, A. Courville, and Y. Bengio, Deep Learning.
MIT press Cambridge, 2016.
[34] S. Böck and M. Schedl, “Polyphonic piano note transcription with
recurrent neural networks,” in Proc. IEEE International Conference on
Acoustics, Speech and Signal Processing, 2012, pp. 121–124.
[35] S. Wang, S. Ewert, and S. Dixon, “Identifying missing and extra notes in
piano recordings using score-informed dictionary learning,” IEEE/ACM
Transactions on Audio, Speech, and Language Processing, vol. 25,
no. 10, pp. 1877–1889, Oct 2017.
[36] X. Serra, “A multicultural approach in music information research,” in
12th International Society for Music Information Retrieval Conference,
2011, pp. 151–156.
Emmanouil Benetos (S’09, M’12) is Lecturer and
Royal Academy of Engineering Research Fellow
with the Centre for Digital Music, Queen Mary
University of London, and Turing Fellow with the
Alan Turing Institute. He received the Ph.D. degree
in Electronic Engineering from Queen Mary Uni-
versity of London, U.K., in 2012. From 2013 to
2015, he was University Research Fellow with the
Department of Computer Science, City, University
of London. He has published over 80 peer-reviewed
papers spanning several topics in audio and music
signal processing. His research focuses on signal processing and machine
learning for music and audio analysis, as well as applications to music
information retrieval, acoustic scene analysis, and computational musicology.
Simon Dixon is Professor and Deputy Director
of the Centre for Digital Music at Queen Mary
University of London. He has a Ph.D. in Computer
Science (Sydney) and L.Mus.A. diploma in Clas-
sical Guitar. His research is in music informatics,
including high-level music signal analysis, compu-
tational modeling of musical knowledge, and the
study of musical performance. Particular areas of
focus include automatic music transcription, beat
tracking, audio alignment and analysis of intonation
and temperament. He was President (2014-15) of the
International Society for Music Information Retrieval (ISMIR), is founding
Editor of the Transactions of ISMIR, and has published over 160 refereed
papers in the area of music informatics.
Zhiyao Duan (S’09, M’13) is an assistant professor
in the Electrical and Computer Engineering Depart-
ment at the University of Rochester. He received
his B.S. in Automation and M.S. in Control Science
and Engineering from Tsinghua University, China, in
2004 and 2008, respectively, and received his Ph.D.
in Computer Science from Northwestern University
in 2013. His research interest is in the broad area
of computer audition, i.e., designing computational
systems that are capable of understanding sounds,
including music, speech, and environmental sounds.
He co-presented a tutorial on Automatic Music Transcription at ISMIR 2015.
He received a best paper award at the 2017 Sound and Music Computing
(SMC) conference and a best paper nomination at ISMIR 2017.
Sebastian Ewert is a Senior Research Scientist at
Spotify. He received the M.Sc./Diplom and Ph.D.
degrees (summa cum laude) in computer science
from the University of Bonn (svd. Max-Planck-
Institute for Informatics), Germany, in 2007 and
2012, respectively. In 2012, he was awarded a GAES
fellowship and joined the Centre for Digital Music,
Queen Mary University of London (United King-
dom). At the Centre, he became Lecturer for Signal
Processing in 2015 and was one of the founding
members of the Machine Listening Lab, which fo-
cuses on the development of machine learning and signal processing methods
for audio and music applications.
... Transcription, or automatic music transcription (AMT), is a music analysis task that aims to represent audio recordings as symbolic notations such as scores or MIDI (Musical Instrument Digital Interface) files [1][2][3]. AMT can play an important role in music information retrieval (MIR) systems since symbolic information -e.g., pitch, duration, and velocity of notes -determines a large part of our musical perception, distinguishing itself from other musical information such as timbre and lyrics. A successful AMT can ease the difficulty of many MIR tasks by providing a denoised version of music in a musically-meaningful, symbolic format. ...
... While automatic music transcription (AMT) models for piano music are well developed and are able to achieve a high accuracy [2,13,[21][22][23][24][25], multi-instrument automatic music transcription (MIAMT) is relatively unexplored. Mu-sicNet [26,27] and ReconVAT [28] are MIAMT systems that transcribe musical instruments other than piano, but their output is a flat piano roll that includes notes from all the instruments in a single channel. ...
... The CNN front end has six convolutional blocks with output channels [64,128,256,512,1024,2048]. Each convolutional block has two convolutional layers with kernel size (3,3), stride (1,1), and padding (1,1), followed by average pooling (2,2). To prevent overfitting, we perform dropout after each convolutional block with a rate of 0.2 [45]. ...
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In this paper, we introduce Jointist, an instrument-aware multi-instrument framework that is capable of transcribing, recognizing, and separating multiple musical instruments from an audio clip. Jointist consists of the instrument recognition module that conditions the other modules: the transcription module that outputs instrument-specific piano rolls, and the source separation module that utilizes instrument information and transcription results. The instrument conditioning is designed for an explicit multi-instrument functionality while the connection between the transcription and source separation modules is for better transcription performance. Our challenging problem formulation makes the model highly useful in the real world given that modern popular music typically consists of multiple instruments. However, its novelty necessitates a new perspective on how to evaluate such a model. During the experiment, we assess the model from various aspects, providing a new evaluation perspective for multi-instrument transcription. We also argue that transcription models can be utilized as a preprocessing module for other music analysis tasks. In the experiment on several downstream tasks, the symbolic representation provided by our transcription model turned out to be helpful to spectrograms in solving downbeat detection, chord recognition, and key estimation.
... Estimation and tracking of multiple fundamental frequencies is one of the major tasks in automatic music transcription (AMT) of polyphonic music analysis [1] and music information retrieval (MIR) [2], which is referred to as a subtask in the Music Information Retrieval Evaluation eXchange (MIREX). 1 Multiple fundamental frequency estimation (MFE), also namely multiple pitch estimation (MPE), is challenging in processing simultaneous notes from multiple instruments in polyphonic music [3,4]. There is often a trade-off between the robustness and efficiency of algorithms that focuses more on complexity rather than single-pitch estimation. ...
... Estimation and tracking of multiple fundamental frequencies is one of the major tasks in automatic music transcription (AMT) of polyphonic music analysis [1] and music information retrieval (MIR) [2], which is referred to as a subtask in the Music Information Retrieval Evaluation eXchange (MIREX). 1 Multiple fundamental frequency estimation (MFE), also namely multiple pitch estimation (MPE), is challenging in processing simultaneous notes from multiple instruments in polyphonic music [3,4]. There is often a trade-off between the robustness and efficiency of algorithms that focuses more on complexity rather than single-pitch estimation. ...
... rate of occurrence in Fig. 4, with the labelled fractions (i.e. 1 2 , 1 4 , 1 8 , 1 16 , 1 32 ) denoting minim, crotchet, quaver, semiquaver and demisemiquaver, respectively. Figure 4 illustrates that the rate of occurrence of ...
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As one of the most important subtasks of automatic music transcription (AMT), multi-pitch estimation (MPE) has been studied extensively for predicting the fundamental frequencies in the frames of audio recordings during the past decade. However, how to use music perception and cognition for MPE has not yet been thoroughly investigated. Motivated by this, this demonstrates how to effectively detect the fundamental frequency and the harmonic structure of polyphonic music using a cognitive framework. Inspired by cognitive neuroscience, an integration of the constant Q transform and a state-of-the-art matrix factorization method called shift-invariant probabilistic latent component analysis (SI-PLCA) are proposed to resolve the polyphonic short-time magnitude log-spectra for multiple pitch estimation and source-specific feature extraction. The cognitions of rhythm, harmonic periodicity and instrument timbre are used to guide the analysis of characterizing contiguous notes and the relationship between fundamental frequency and harmonic frequencies for detecting the pitches from the outcomes of SI-PLCA. In the experiment, we compare the performance of proposed MPE system to a number of existing state-of-the-art approaches (seven weak learning methods and four deep learning methods) on three widely used datasets (i.e. MAPS, BACH10 and TRIOS) in terms of F-measure (F1) values. The experimental results show that the proposed MPE method provides the best overall performance against other existing methods.
... The input parameters of synthesizers can be studied from the two perspectives of generating and analyzing music. Whereas the former is studied in the context of virtual instrument design, the latter is studied in the context of Music Performance Analysis [170] and, more interestingly, of Automatic Music Transcription (AMT) [171]. Specifically, in this discussion, we will refer to AMT as a signal processing task that converts an input audio to the corresponding synthesizer parameters. ...
... Nowadays, AMT is a broad signal processing field encompassing a wide gamut of tasks and approaches. As an example, the output of an AMT system can be a traditional score, a Standard MIDI File (SMF), or a set of ad-hoc features [171]. A traditional score is a sequence of symbols that describes music according to the western notation and focuses on expressing music in a human-readable way so that it can be easily reproduced. ...
... In recent years, various studies about Music Performance Analysis (MPA) [170] have faced the problem of Automatic Music Transcription (AMT) [171], especially in piano music. Almost every year, a new state-of-the-art model is published in major conferences and journals [194,280,281,325,335,336], and, importantly, current models have achieved an increase of almost 10% in F1-measure since 2018. ...
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This Thesis discusses the development of technologies for the automatic resynthesis of music recordings using digital synthesizers. First, the main issue is identified in the understanding of how Music Information Processing (MIP) methods can take into consideration the influence of the acoustic context on the music performance. For this, a novel conceptual and mathematical framework named "Music Interpretation Analysis" (MIA) is presented. In the proposed framework, a distinction is made between the "performance" - the physical action of playing - and the "interpretation" - the action that the performer wishes to achieve. Second, the Thesis describes further works aiming at the democratization of music production tools via automatic resynthesis: 1) it elaborates software and file formats for historical music archiving and multimodal machine-learning datasets; 2) it explores and extends MIP technologies; 3) it presents the mathematical foundations of the MIA framework and shows preliminary evaluations to demonstrate the effectiveness of the approach
... The problem of univocal characterization of musical timbre raises the need to elaborate descriptors (derived magnitudes, coefficients, or functional) that evaluate timbre from digital audio records [7]. This is important for problems such as Automatic Music Transcription (AMT) [8]. It has been identified that, for audio recordings, it is necessary to have efficient systems that identify the different musical timbres with high precision and quantitatively [9]. ...
... If we restrict ourselves to the studied aerophones, which are only a small sample of all common musical instruments, a discriminatory identification between them can be made from the presented FFT spectra for common records in the range of B3 sounds to D#5, as shown in Figure 15. The harmonic frequencies ( ) are those that are related to the fundamental frequency through the integer multiplicity (n = 2, 3, 4, …): (8) It is the case that not all the harmonics of a certain musical sound are always present. This allows, in principle, to discriminate the sounds of one of the aerophones from the others (Table 1). ...
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... e basic idea is to propagate the output layer error from back to front layer by layer, and indirectly calculate the hidden layer error [22]. e structure diagram is shown in Figure 4. e BP algorithm is now applied to music [23]: ...
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Music is a common art and it is a jewel of human civilisation. In the course of music’s development, the evaluation of music teaching is an inevitable step in the development of quality music. Universities are important places that abound with musical souls and their contribution to the development of music has been outstanding. But with the development of the times, university music has been hampered in the field of teaching and learning. As an important branch in the field of computer science and information technology, artificial intelligence technology contains many intersecting and comprehensive subject connotations, bringing brand-new elements to music education. It has also had an important impact on the development of music teaching. The article focuses in depth on the traditional process of music development in terms of the characteristics and ways of teaching music. Based on this, the article further explores the integration of artificial intelligence and music and analyses the role of emerging technologies as an aid to music from the perspective of the times. And this article uses emotion recognition as an evaluation index to explore the evaluation role of artificial intelligence technology in college music teaching, and improve the quality and efficiency of music teaching. The experimental results show that the teacher’s positive emotion rate based on image data is 57.8%, and the student’s positive emotion rate is 44.5%; the teacher’s positive emotion rate based on voice data is 53.3%, and the student’s positive emotion rate is 51.1%. The classroom emotion is negative at 7–13 minutes, the classroom emotion continues to be low at 28–40 minutes, and the teacher and student emotions are more positive at 13–28 minutes.
... ere is not much literature on polyphonic score recognition, but research in this area has increased in the last two years. Reference [27] proposed a method for analyzing polyphonic music scores based on a dynamic Bayesian network. eir approach, which emphasizes the modeling of sound production processes, enables the tracking of beat and pitch trajectories of polyphonic music. ...
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Music education is an essential and significant link in a quality education, as it can assist pupils improve their integrity and nurture noble character. The evident distinction between music teaching and teaching in other disciplines is that music teaching can provide aesthetic education to students in order to improve students’ self-cultivation and overall temperament and basically play a role in developing people in a holistic fashion. Note detection is an important content in music teaching. Instrument tuning, computerized score recognition, music database search, and electronic music synthesis all benefit greatly from note detection technologies. In note detection, there are problems such as difficult one-to-one correspondence between estimated pitches and standard frequencies, a narrow range of identifiable pitches, poor robustness of the recognition process, and low recognition rate. In this context, this work proposes an automatic note detection in music teaching based on deep learning. It uses a convolutional neural network (CNN) and a bidirectional long-short-term memory (BiLSTM) network to build a deep neural network model, called convolutional neural network Bidirectional Long Short-Term Memory (CNN-BiLSTM), using this network to conduct in-depth research on note detection. First, based on the current research status, a deep neural network model based on CNN and BiLSTM is proposed to detect musical notes. The network can independently mine and learn the deep-level features of music signals and has better feature extraction and generalization capabilities. Second, the experimental results are evaluated using different evaluation metrics. Experiments show the network model can significantly improve detection accuracy and can efficiently detect notes in music teaching.
... An alternative method to obtain independent F0 trajectories for each singer is to address the task of multi-pitch streaming (MPS). MPS is described by Benetos et al. (2019) as grouping estimated pitches or notes into streams, where each stream typically corresponds to one instrument or musical voice. When we combine an MPE system with a VA one, the result is a system that yields independent F0 contours for each source in the input audio mixture just as in MPS. ...
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This paper deals with the automatic transcription of four-part, a cappella singing, audio performances. In particular, we exploit an existing, deep-learning based, multiple F0 estimation method and complement it with two neural network architectures for voice assignment (VA) in order to create a music transcription system that converts an input audio mixture into four pitch contours. To train our VA models, we create a novel synthetic dataset by collecting 5381 choral music scores from public-domain music archives, which we make publicly available for further research. We compare the performance of the proposed VA models on different types of input data, as well as to a hidden Markov model-based baseline system. In addition, we assess the generalization capabilities of these models on audio recordings with differing pitch distributions and vocal music styles. Our experiments show that the two proposed models, a CNN and a ConvLSTM, have very similar performance, and both of them outperform the baseline HMM-based system. We also observe a high confusion rate between the alto and tenor voice parts, which commonly have overlapping pitch ranges, while the bass voice has the highest scores in all evaluated scenarios.
... Audio can be transcribed into MusicXML and MIDI via Automatic Music Transcription (AMT) [54] or by human transcription. On the one hand, AMT is a difficult problem, and-so far-no techniques can match the performance of human experts [55]. ...
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Real-time tracking of the position of a musical performance on a musical score, i.e. score following, can be useful in music practice, performance and production. Example applications of such technology include computer-aided accompaniment and automatic page turning. Score following is a challenging task, especially when considering deviations in performance data from the score stemming from mistakes or expressive choices. In this project, the extensive research present in the field is first explored before two open-source evaluation testbenches for score following--one quantitative and the other qualitative--are introduced. A new way of obtaining quantitative testbench data is proposed, and the QualScofo dataset for qualitative benchmarking is introduced. Subsequently, three different score followers, each of a different class, are implemented. First, a beat-based follower for an interactive conductor application--the TuneApp Conductor--is created to demonstrate an entertaining application of score following. Then, an Approximate String Matching (ASM) non-real-time follower is implemented to complement the quantitative testbench and provide more technical background details of score following. Finally, a Constant Q-Transform (CQT) Dynamic Time Warping (DTW) score follower robust against major challenges in score following (such as polyphonic music and performance deviations) is outlined and implemented; it is shown that this CQT-based approach consistently and significantly outperforms a commonly used FFT-based approach in extracting audio features for score following.
... Automatic Music Transcription (AMT) is a well-known task within the Music Information Retrieval (MIR) community dealing with the estimation of note content within a music signal [1]. Guitar tablature transcription refers to the specific problem of estimating all of the notes within a solo guitar recording and identifying the strings that were used to play them. ...
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Guitar tablature transcription is an important but understudied problem within the field of music information retrieval. Traditional signal processing approaches offer only limited performance on the task, and there is little acoustic data with transcription labels for training machine learning models. However, guitar transcription labels alone are more widely available in the form of tablature, which is commonly shared among guitarists online. In this work, a collection of symbolic tablature is leveraged to estimate the pairwise likelihood of notes on the guitar. The output layer of a baseline tablature transcription model is reformulated, such that an inhibition loss can be incorporated to discourage the co-activation of unlikely note pairs. This naturally enforces playability constraints for guitar, and yields tablature which is more consistent with the symbolic data used to estimate pairwise likelihoods. With this methodology, we show that symbolic tablature can be used to shape the distribution of a tablature transcription model's predictions, even when little acoustic data is available.
... Automatic music transcription (AMT) aims to convert music signals into music notation. It is of great importance to solve the AMT problem because the transcription results can be helpful in many higherlevel tasks, like structure segmentation, music similarity assessment, and so on [1]. However, it is not easy to provide a generic solution to AMT, since a music piece usually contains multiple different sound sources and lots of simultaneous notes. ...
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Most recent research about automatic music transcription (AMT) uses convolutional neural networks and recurrent neural networks to model the mapping from music signals to symbolic notation. Based on a high-resolution piano transcription system, we explore the possibility of incorporating another powerful sequence transformation tool -- the Transformer -- to deal with the AMT problem. We argue that the properties of the Transformer make it more suitable for certain AMT subtasks. We confirm the Transformer's superiority on the velocity detection task by experiments on the MAESTRO dataset and a cross-dataset evaluation on the MAPS dataset. We observe a performance improvement on both frame-level and note-level metrics after introducing the Transformer network.
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This paper presents a novel approach to automatic transcription of piano music in a context-dependent setting. This approach employs convolutional sparse coding to approximate the music waveform as the summation of piano note waveforms (dictionary elements) convolved with their temporal activations (onset transcription). The piano note waveforms are pre-recorded for the specific piano to be transcribed in the specific environment. During transcription, the note waveforms are fixed and their temporal activations are estimated and post-processed to obtain the pitch and onset transcription. This approach works in the time domain, models temporal evolution of piano notes, and estimates pitches and onsets simultaneously in the same framework. Experiments show that it significantly outperforms a state-of-the-art music transcription method trained in the same context-dependent setting, in both transcription accuracy and time precision, in various scenarios including synthetic, anechoic, noisy, and reverberant environments.
Conference Paper
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In an attempt at exploring the limitations of simple approaches to the task of piano transcription (as usually defined in MIR), we conduct an in-depth analysis of neural network-based framewise transcription. We systematically compare different popular input representations for transcription systems to determine the ones most suitable for use with neural networks. Exploiting recent advances in training techniques and new regularizers, and taking into account hyper-parameter tuning, we show that it is possible , by simple bottom-up frame-wise processing, to obtain a piano transcriber that outperforms the current published state of the art on the publicly available MAPS dataset – without any complex post-processing steps. Thus, we propose this simple approach as a new baseline for this dataset, for future transcription research to build on and improve.
Conference Paper
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Even though originally developed for exchanging control commands between electronic instruments, MIDI has been used as quasi standard for encoding and storing score- related parameters. MIDI allows for representing musi- cal time information as specified by sheet music as well as physical time information that reflects performance as- pects. However, in many of the available MIDI files the musical beat and tempo information is set to a preset value with no relation to the actual music content. In this pa- per, we introduce a procedure to determine the musical beat grid from a given performed MIDI file. As one main contribution, we show how the global estimate of the time signature can be used to correct local errors in the pulse grid estimation. Different to MIDI quantization, where one tries to map MIDI note onsets onto a given musical pulse grid, our goal is to actually estimate such a grid. In this sense, our procedure can be used in combination with existing MIDI quantization procedures to convert per- formed MIDI files into semantically enriched score-like MIDI files. Estimating Musical Time Information from Performed MIDI Files | Request PDF. Available from: [accessed Sep 02 2018].
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We present a neural network model for polyphonic music transcription. The architecture of the proposed model is analogous to speech recognition systems and comprises an acoustic model and a music language mode}. The acoustic model is a neural network used for estimating the probabilities of pitches in a frame of audio. The language model is a recurrent neural network that models the correlations between pitch combinations over time. The proposed model is general and can be used to transcribe polyphonic music without imposing any constraints on the polyphony or the number or type of instruments. The acoustic and language model predictions are combined using a probabilistic graphical model. Inference over the output variables is performed using the beam search algorithm. We investigate various neural network architectures for the acoustic models and compare their performance to two popular state-of-the-art acoustic models. We also present an efficient variant of beam search that improves performance and reduces run-times by an order of magnitude, making the model suitable for real-time applications. We evaluate the model's performance on the MAPS dataset and show that the proposed model outperforms state-of-the-art transcription systems.
This book presents computational methods for extracting the useful information from audio signals, collecting the state of the art in the field of sound event and scene analysis. The authors cover the entire procedure for developing such methods, ranging from data acquisition and labeling, through the design of taxonomies used in the systems, to signal processing methods for feature extraction and machine learning methods for sound recognition. The book also covers advanced techniques for dealing with environmental variation and multiple overlapping sound sources, and taking advantage of multiple microphones or other modalities. The book gives examples of usage scenarios in large media databases, acoustic monitoring, bioacoustics, and context-aware devices. Graphical illustrations of sound signals and their spectrographic representations are presented, as well as block diagrams and pseudocode of algorithms. • Gives an overview of methods for computational analysis of sounds scenes and events, allowing those new to the field to become fully informed; • Covers all the aspects of the machine learning approach to computational analysis of sound scenes and events, ranging from data capture and labeling process to development of algorithms; • Includes descriptions of algorithms accompanied by a website from which software implementations can be downloaded, facilitating practical interaction with the techniques.
We consider the problem of transcribing polyphonic piano music with an emphasis on generalizing to unseen instruments. We use deep neural networks and propose a novel approach that predicts onsets and frames using both CNNs and LSTMs. This model predicts pitch onset events and then uses those predictions to condition framewise pitch predictions. During inference, we restrict the predictions from the framewise detector by not allowing a new note to start unless the onset detector also agrees that an onset for that pitch is present in the frame. We focus on improving onsets and offsets together instead of either in isolation as we believe it correlates better with human musical perception. This technique results in over a 100% relative improvement in note with offset score on the MAPS dataset.
The goal of automatic music transcription (AMT) is to obtain a high-level symbolic representation of the notes played in a given audio recording. Despite being researched for several decades, current methods are still inadequate for many applications. To boost the accuracy in a music tutoring scenario, we exploit that the score to be played is specified and we only need to detect the differences to the actual performance. In contrast to previous work which uses score information for post-processing, we employ the score to construct a transcription method that is tailored to the given audio recording. By adapting a score- informed dictionary learning technique as used for source separation, we learn for each score pitch a spectral pattern describing the energy distribution of associated notes in the recording. In this paper, we identify several systematic weaknesses in our previous approach and introduce three extensions to improve its performance. Firstly, we extend our dictionary of spectral templates to a dictionary of variable-length spectro-temporal patterns. Secondly, we integrate the score information using soft rather than hard constraints, to better take into account that differences from the score indeed occur. Thirdly, we introduce new regularizers to guide the learning process. Our experiments show that these extensions particularly improve the accuracy for identifying extra notes, while the accuracy for correct and missing notes remains at a similar level. The influence of each extension is demonstrated with further experiments.
Conference Paper
While recent years have witnessed large progress in the algorithm of automatic music transcription (AMT), the development of general and sizable datasets for AMT evaluation is relatively stagnant, predominantly due to the fact that manually annotating and checking such datasets is labor-intensive and time-consuming. In this paper we propose a novel note-level annotation method for building AMT datasets by utilizing human?s ability in following music in real-time. To test the quality of the annotation, we further propose an efficient method in qualifying an AMT dataset based on the concepts of onset error difference and the tolerance computed from the evaluation result. According to the experiments on five piano solos and four woodwind quintets, we claim that the proposed annotation method is reliable for evaluation of AMT algorithms.
Given a musical audio recording, the goal of automatic music transcription is to determine a score-like representation of the piece underlying the recording. Despite significant interest within the research community, several studies have reported on a 'glass ceiling' effect, an apparent limit on the transcription accuracy that current methods seem incapable of overcoming. In this paper, we explore how much this effect can be mitigated by focusing on a specific instrument class and making use of additional information on the recording conditions available in studio or home recording scenarios. In particular, exploiting the availability of single note recordings for the instrument in use we develop a novel signal model employing variable-length spectro-temporal patterns as its central building blocks - tailored for pitched percussive instruments such as the piano. Temporal dependencies between spectral templates are modeled, resembling characteristics of factorial scaled hidden Markov models (FS-HMM) and other methods combining Non-Negative Matrix Factorization with Markov processes. In contrast to FS-HMMs, our parameter estimation is developed in a global, relaxed form within the extensible alternating direction method of multipliers (ADMM) framework, which enables the systematic combination of basic regularizers propagating sparsity and local stationarity in note activity with more complex regularizers imposing temporal semantics. The proposed method achieves an f-measure of 93-95% for note onsets on pieces recorded on a Yamaha Disklavier (MAPS DB).