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Deep Learning and Intelligent Audio Mixing



Mixing multitrack audio is a crucial part of music production. With recent advances in machine learning techniques such as deep learning, it is of great importance to conduct research on the applications of these methods in the field of automatic mixing. In this paper, we present a survey of intelligent audio mixing systems and their recent incorporation of deep neural networks. We propose to the community a research trajectory in the field of deep learning applied to intelligent music production systems. We conclude with a proof of concept based on stem audio mixing as a content-based transformation using a deep autoencoder.
Proceedings of the 3rd Workshop on Intelligent Music Production, Salford, UK, 15 September 2017
Marco A. Mart´
ınez Ram´
ırez, Joshua D. Reiss
Centre for Digital Music
Queen Mary University of London
Mixing multitrack audio is a crucial part of music produc-
tion. With recent advances in machine learning techniques
such as deep learning, it is of great importance to conduct
research on the applications of these methods in the field
of automatic mixing. In this paper, we present a survey of
intelligent audio mixing systems and their recent incorpora-
tion of deep neural networks. We propose to the community
a research trajectory in the field of deep learning applied to
intelligent music production systems. We conclude with a
proof of concept based on stem audio mixing as a content-
based transformation using a deep autoencoder.
Audio mixing essentially tries to solve the problem of un-
masking by manipulating the dynamics, spatialisation, tim-
bre or pitch of multitrack recordings.
Automatic mixing has been investigated as an extension
of adaptive digital audio effects [1] and content-based trans-
formations [2]. This is because the processing of an individ-
ual track depends on the content of all the tracks involved,
thus audio features are extracted and mapped for the imple-
mentation of cross-adaptive systems [3].
The most informed expert knowledge framework for au-
tomatic mixing applications can be found in [4] and [5]. In
which the potential assumptions for technical audio mixing
decisions were validated through various strategies.
In the same way, audio features have been analysed to
gain a better understanding of the mixing process or to per-
form different tasks within an automatic mixing framework.
In [6], low-level features were extracted from a set of mix-
ing sessions and their variance was analysed between in-
struments, songs and sound engineers. [7] extracted features
from 1501 mixes and proposed that high-quality mixes are
located in certain areas of the feature space.
Likewise, machine learning techniques have been ap-
plied to the estimation of mixing coefficients through linear
dynamical systems [8] and least-squares optimization mod-
els [9]. [10] used random forests classifiers and agglomera-
tive clustering for automatic subgrouping of multitrack au-
dio. Also, by means of an interactive genetic algorithm, [11]
proposed an exploration of a subjective mixing space.
Through the different listening tests of these models, the
systems proved to perform better than amateur sound engi-
neers, but experienced sound engineer mixes were always
preferred. In addition, most of the models were aimed at a
technically clean mix, yet audio mixing often includes dif-
ferent types of objectives such as transmitting a particular
feeling, creating abstract structures, following trends, or en-
hancing [12]. Also, the models cannot be extended to com-
plex or unusual sounds. Thus past attempts fall short, since
a mix that only fulfils basic technical requirements cannot
compete with the results of an experienced sound engineer.
”The intelligence is in the sound” [1]
Therefore, machine learning techniques together with
expert knowledge, may be able to guide towards the devel-
opment of a system capable of performing automatic mixing
or to assist the sound engineer during the mixing process.
We propose to the community a research outline within the
framework of automatic mixing and deep neural networks.
The rest of the paper is organised as follows. In Section
2 we summarise the relevant literature related to deep learn-
ing and music. We propose a research outline in Section 3
and in Section 4 we present a proof of concept. Finally, we
conclude with Section 5.
2.1. Deep learning and music
In recent years, deep neural networks (DNN) applied to mu-
sic have experienced a significant growth. A large percent-
age of current research has been devoted to extract informa-
tion and understand its content. Most of the examples are
in the fields of music information retrieval [13, 14], music
recommendation [15, 16], and audio event recognition [17].
Nonetheless, deep learning applied to the generation of
music has become a growing field with emerging projects
such as Magenta and its Nsynth [18]: A Wavenet [19] au-
toencoder that generates audio sample by sample and allows
instrument morphing from a dataset of short musical notes.
This was achieved using an end-to-end architecture, where
raw audio is both the input and the output of the system.
Similarly, [20] obtained raw audio generation without the
need of handcrafted features and [21] accomplished singing
voice synthesis.
Also, as an alternative to working directly with audio
samples other architectures have being explored. [22] trained
Licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Proceedings of the 3rd Workshop on Intelligent Music Production, Salford, UK, 15 September 2017
a Long Short Term Memory (LSTM) recurrent neural net-
work (RNN) architecture together with Reinforcement Learn-
ing (RL) to predict the next note in a musical sequence. [23]
used a character-based model with a LSTM to generate a
textual representation of a song.
2.1.1. Deep learning and music production
Recent work has demonstrated the feasibility of deep learn-
ing applied to intelligent music production systems. [24]
used a convolutional DNN and supervised learning to ex-
tract vocals from a mix. [25] used DNNs for the separa-
tion of solo instruments in jazz recordings and the remixing
of the obtained stems. Similary, [26] used a DNN to per-
form audio source separation in order to remix and upmix
existing songs. [27] relied on pre-trained autoencoders and
critical auditory bands to perform automatic dynamic range
compression (DRC) for mastering applications. This was
achieved using supervised and unsupervised learning meth-
ods to unravel correlations between unprocessed and mas-
tered audio tracks.
Thus, it is worth noting the immense benefit that gen-
erative music could obtain from intelligent production tools
and vice versa.
We seek to explore how can we train a deep neural net-
work to perform audio mixing as a content-based transfor-
mation without using directly the standard mixing devices
(e.g. dynamic range compressors, equalizers, limiters, etc.).
Following an end-to-end architecture, we propose to inves-
tigate whether a DNN is able to learn and apply the intrinsic
characteristics of this transformation.
In addition, we investigate how can a deep learning sys-
tem use expert knowledge to improve the results within a
mixing task. In this way, based on a set of best practices, we
explore how to expand the possibilities of a content-based
audio mixing system.
Similarly, we research whether the system can perform
a goal-oriented mixing task and if we can guide it with fea-
tures extracted from a mixdown. This, taking into account
statistical models used in style-imitation or style-transfer
[28, 29]. We seek to investigate these principles within an
audio mixing framework.
We explore if the system can act alongside human action
as a technical assistant, and also whether we can integrate
user interaction as a final fine-tuning of the mixing process.
We investigate a tool capable of guaranteeing technical cri-
teria while learning from the user. Thus, by making the user
an integral part of the system, we take advantage of both the
memory and processing power of the machine as well as the
intuitive and synthesizing ability of the user [30].
Processing individual stems from raw recordings is one of
the first steps of multitrack audio mixing. We investigate
stem processing as a content-based transformation, where
the frequency content of raw recordings (input) and stems
(target) leads us to train a deep autoencoder (DAE). Thus,
we are exploring whether the system is capable of learning a
transformation that applies the same chain of audio effects.
The raw recordings and individual processed stems were
taken from [31], mostly based on [32] and following the
same structure; a song consists of the mix, stems and raw
audio. The dataset consists of 102 multitracks which corre-
spond to genres of commercial western music. Each track
was mixed and recorded by professional sound engineers.
All tracks have a sampling frequency of 44.1 kHz, and
we proceeded to find the 10 seconds with the highest energy
for each stem track. Thus, the corresponding raw tracks
were then analysed and the one with the highest energy in
the same 10 second interval was chosen.
The selected segments were downmixed to mono and
loudness normalisation was performed using [33]. Data aug-
mentation was implemented by pitch shifting each raw and
stem track by ±4semitones in intervals of 50 cents. The
frequency magnitude was computed with frame/hop sizes
equal to 2048/1024 samples. The test dataset corresponds
to 10% of the raw and stem segments and data augmenta-
tion was not applied.
Table 1: Number of raw/stem tracks and augmented seg-
Group Instrument Source Raw Stem Augmented
Bass electric bass 96 62 1020
synth bass 12 6
clean electric guitar 112 36
acoustic guitar 55 24
distorted electric guitar 78 20
banjo 2 2
male singer 145 36
969female singer 61 22
male rapper 12 2
piano 113 38
synth lead 51 17
tack piano 27 7
electric piano 3 3
The DAEs consist of feed-forward stacked autoencoders.
Each hidden layer was trained using the greedy layer-wise
approach [34], dropout with a probability of 0.2, Adam as
optimizer, reLu as activation function, and mean absolute
error as loss function. In total, each DAE has 3 hidden lay-
ers of 1024 neurons and input and output layers of 1025
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Proceedings of the 3rd Workshop on Intelligent Music Production, Salford, UK, 15 September 2017
(a) (b)
(c) (d)
(e) (f)
Figure 1: Bass, waveforms and spectrograms. (a), (b) Raw
input. (c), (d) Stem target. (e), (f) DAE’s output .
(a) (b)
(c) (d)
(e) (f)
Figure 2: Guitar, waveforms and spectrograms. (a), (b) Raw
input. (c), (d) Stem target. (e), (f) DAE’s output .
The DAEs were trained independently for each instru-
ment group. After 100 learning iterations, Figs. 1-4 show
the results of the DAEs when tested with a raw recording
from the test dataset. The waveform was reconstructed with
the original phase of the raw segments.
It can be seen that the output contains artefacts and noise
introduced by the DAEs, however the main harmonics and
envelope has been maintained. Among the groups, the DAEs
performed better for Bass and Guitar than for Vocal and
Keys. This is because vocal sounds are much more complex
than the other instruments groups. Also, the Keys’ instru-
(a) (b)
(c) (d)
(e) (f)
Figure 3: Vocal, waveforms and spectrograms. (a), (b) Raw
input. (c), (d) Stem target. (e), (f) DAE’s output .
(a) (b)
(c) (d)
(e) (f)
Figure 4: Keys, waveforms and spectrograms. (a), (b) Raw
input. (c), (d) Stem target. (e), (f) DAE’s output .
ments have a higher variance of roles within the different
genres. Further analysis is required as well as listening and
similarity tests to correctly measure the performance of the
The current research is at an early stage and the DAE ar-
chitecture is considerably simple. We plan to incorporate
already successful end-to-end architectures to achieve a sys-
Licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Proceedings of the 3rd Workshop on Intelligent Music Production, Salford, UK, 15 September 2017
tem capable of performing stem audio mixing as a content-
based transformation.
Intelligent music production systems have the potential
to benefit from deep learning techniques applied to music
generation and vice-versa. We encourage the community to
investigate the proposed research questions. In this way, an
intelligent system capable of perform automatic mixing or
to assist the sound engineer during the mixing process could
be possible.
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... There is growing scope for the use of Deep Neural Network (DNN) approaches in the use of automatic mixing. Martínez Ramírez and Reiss [166] identify that previous automatic mixing approaches do not capture the highly nonlinear approaches. Since then, there have been several approaches towards using DNN for automatic mastering, including source separation and remixing [167], automatic mixing through audio feature transformation [168,169], or some audio mixing style transfer approach [170]. ...
... The technology evolutions have had a steady impact on the range of audio effects available; however, there seems to be little work encompassing the latest growth in machine learning technologies to the use of audio effects. There is use of these technologies to model and represent existing audio effects [171][172][173] to represent the entire mixing process [166] or to improve the ability for audio cleanup technologies [176]. However, although we have identified some examples in this article, there has been limited use of this technological advancement to produce new types of audio effects or to facilitate the ability for mix engineers to interact with audio in a completely new way. ...
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Audio effects are an essential tool that the field of music production relies upon. The ability to intentionally manipulate and modify a piece of sound has opened up considerable opportunities for music making. The evolution of technology has often driven new audio tools and effects, from early architectural acoustics through electromechanical and electronic devices to the digitisation of music production studios. Throughout time, music has constantly borrowed ideas and technological advancements from all other fields and contributed back to the innovative technology. This is defined as transsectorial innovation and fundamentally underpins the technological developments of audio effects. The development and evolution of audio effect technology is discussed, highlighting major technical breakthroughs and the impact of available audio effects.
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