# “Melatonin”: A Case Study on AI-induced
Musical Style

Emmanuel Deruty
Sony Computer Science Laboratories (CSL) Paris
6 Rue Amyot
75005 Paris, France
https://csl.sony.fr/team/emmanuel-deruty/
Maarten Grachten
Contractor for Sony CSL Paris
6 Rue Amyot
75005 Paris, France
https://maarten.grachten.eu

###### Abstract

Although the use of AI tools in music composition and production is steadily
increasing, as witnessed by the newly founded AI song contest, analysis of
music produced using these tools is still relatively uncommon as a mean to
gain insight in the ways AI tools impact music production. In this paper we
present a case study of “Melatonin”, a song produced by extensive use of
BassNet, an AI tool originally designed to generate bass lines. Through
analysis of the artists’ work flow and song project, we identify style
characteristics of the song in relation to the affordances of the tool,
highlighting manifestations of style in terms of both idiom and sound.

##  1 Introduction and Related Work

AI is rapidly gaining foothold in the workflow of music artists, bringing
exciting opportunities for musical creativity. Music AI tools offer novel
functionality and affordances (McGrenere and Ho,, 2000), and a particularly
interesting question in this context is whether, and how, this novelty affects
stylistic aspects of music made using these tools.

Obviously it is impossible to provide a general response to this question, not
least because music AI tools form a very broad category that is only just
emerging, but also because there are no established practices yet for the use
of AI tools in music. Nevertheless we believe it is instructive to study how
individual music artists use music AI tools, and how these shape the resulting
music, as it can further our understanding of what it is that allows AI tools
to bring value to the music production workflow, and thereby help to improve
the design and effectiveness of the tools.

We intend to contribute to this aim by way of a case study: we focus on a
collaboration between music artist duo Hyper Music111https://www.hyper-
music.com/ and the music research group of Sony Computer Science Laboratories
(CSL) Paris222https://cslmusicteam.sony.fr/, in which Hyper Music uses a CSL-
developed AI tool to produce new songs. We focus particularly on stylistic
aspects of the resulting music, drawing on definitions from Pascall, (2001).

This focus on the musical output itself, rather than feedback from the
artists, makes our work complementary to works like (Huang et al.,, 2020) and
(Deruty et al.,, 2022), where thematic analyses are made based on feedback
from the artists/users reporting on their experience using the tools. Other
works evaluate music AI tools by qualitative judgments of value by humans. For
example, Cadiz et al., (2021) ask human subjects to rate the creativity of
generated chords and MIDI piano excerpts. Collins et al., (2016) report on a
listening experiment in which subjects rate the degree to which the outputs of
generative computational models of stylistic composition adhere to the
characteristics of the intended style (Chopin’s Mazurkas). Sturm et al.,
(2016) evaluate a model of Celtic folk melodies by comparing the outputs to
the training data in terms of statistical properties, by qualitative
evaluation of the stylistic features of the outputs, and through the utility
of the model as a compositional tool. The above works focus on style in the
context of symbolic representations of music. As for style in the context of
audio, existing work deals predominantly with the problem of _style transfer_
, e.g. transformation between music instruments Liu et al., (2019), or
morphing between sounds such as speech and music Grinstein et al., (2018).

Our contributions in this paper are the following. Firstly, we document the
usage of a music AI tool by professional artists, as well as changes made to
the tool in response to the artist’s suggestions. Secondly, we provide an
analysis of a selection of the musical outputs in terms of their stylistic
characteristics, establishing a link between these characteristics and the
affordances of the AI tool.

The structure of the paper is as follows. In Section 2 we introduce the notion
of musical style. In Section 3 we describe the form and the specific aims of
the collaboration, as well as the methods of interpreting the musical outputs.
Section 4 details how the AI tools were used in the music production process,
and presents an analysis of selected musical outputs in terms of musical
style. We discuss the results in Section 5, and present our conclusions in
Section 6.

##  2 Background: What is Musical Style?

To address the question whether and how the AI tools used by Hyper Music
affect the musical style of the outputs, we must first find an operational
definition of the term. According to the Merriam Webster dictionary, _style_
is defined as “a distinctive manner of expression” (Merriam-Webster,, 2022).
In the field of music, style may qualify a variety of entities. According to
(Pascall,, 2001), style refers to the manner in which a work of art is
executed, and can denote music characteristic of an individual composer, a
period, a geographical area or center, or of a society or social function.

For Parry, (1911), musical style originates in the relationship between
resources and utterance. As such, style may also be linked to the resources or
means by which the music is produced. This holds notably for the instruments
used. Firstly, they may introduce characteristic _sounds_ , but by their
specific affordances and limitations they may also lead to specific _idioms_
of expression. Some of the examples Pascall, (2001) gives in this context are
the violin with its capacity for wide-ranging melody and high tessitura, and
the organ, where alternating feet on the bass pedals produce patterns that are
distinct for late Baroque German organ music. In contemporary Popular Music,
tools used for generating and processing audio may play an equally prominent
role in defining style. Examples include the analog synthesizer, the
distortion pedal, and more recently the ubiquitous Antares Auto-Tune plugin.

Following (Pascall,, 2001), we distinguish between two manifestations of
style, namely (1) the characteristic sounds stemming from the use of a given
technology and (2) the idiomatic possibilities deriving from the technology.
We use these two aspects of style in our analysis of the outputs produced by
Hyper Music.

##  3 Data and Method

###  3.1 Collaboration between Hyper Music and CSL

Hyper Music is a sound and music production company that produces music for
ads, TV series and feature films. Over the years, Hyper Music has produced a
catalog of music tracks. When potential clients require music for an ad, they
advertise their need and Hyper Music submits a track from their catalog. If
the track is selected for the ad, Hyper Music customizes the track for the
client. In that sense, Hyper Music is a provider of Production Music (Crain,,
2018).

The music research group of Sony Computer Science Laboratories (CSL) Paris
develops AI-based music tools to support music artists in their creative
workflows. To reinforce the practical relevance, development is done in
parallel with collaborations in which professional music artists use
prototypes of the tools. Some of the collaborations have been reported by
Deruty et al., (2022). The collaborations take place under an agreement where
the artists agree to deliver one or more songs, making use of the AI tools
provided by CSL.

An earlier collaboration of CSL with Hyper Music focused on embedding AI tools
in their workflow with the aim of producing songs that would fit into the
Hyper Music portfolio. This collaboration was successful in its own right, in
the sense that the music created with the help of CSL tools was used as the
soundtrack in a worldwide Azzaro commercial. Nevertheless, the contribution of
the AI tools in this material was subtle and restricted to interventions that
did not noticeably alter the musical style—a consequence of the need of Hyper
Music to maintain the trademark style of their portfolio.

To get a clearer view on the effect AI tools may have on musical style, a
follow-up collaboration was planned, with a different specification of the
task. In this collaboration, the goal was to be more experimental and center
the songs around the outputs of the AI tools, without the need for the
resulting songs to fit into the portfolio. This is not to say the aim was to
produce _experimental music_ —a musical genre characterized by its radical
opposition to and questioning of institutionalized modes of composition,
performance, and aesthetics (Sun,, 2012). On the contrary, the artists were
asked not deviate from the codes of mainstream Popular Music they typically
adhere to: there should be a stable tempo, an alternation between kick drum
and snare drum, a reference to tonality, and a recognizable large-scale
structure.

This follow-up collaboration took place over the course of 2021, and resulted
in five tracks, using various CSL tools in the process. Most of these tools
have been described by Deruty et al., (2022). In this paper we will focus on
one of the five tracks – titled “Melatonin”, which came about during a
dedicated four-day collaborative session between Hyper Music and CSL, centered
on CSL’s _BassNet_ tool (Grachten et al.,, 2020). During this session CSL
research scientists assisted Hyper Music with the use of the tool, observed
the production strategies Hyper Music employed, and made changes to the
BassNet prototype in response to suggestions by Hyper Music.

###  3.2 BassNet

BassNet is a music AI tool by CSL, designed to create bass lines to accompany
existing audio material provided by the user (we refer to the latter as
_conditioning material/audio_). It takes any mix of existing audio (e.g.
drums, chords, vocals) as input, and produces bass tracks both in audio and
MIDI format. It relies on a neural network architecture that models the
relationship between bass lines and accompanying material through a
2-dimensional latent space, which can be used as a control parameter by the
user to create multiple variations of bass lines for the same conditioning
material. See (Grachten et al.,, 2020) for more details, or the BassNet
introduction web page for a non-technical overview and demonstration video.

###  3.3 Methods of Analysis

Our analysis consists of a study of production strategies used by Hyper Music
to produce the audio material of the song, as well as the stylistic aspects of
the produced material, in particular the tracks generated using BassNet.

Based on a log of the production process kept by CSL researchers and the
Digital Audio Workstation session of the song produced by Hyper Music, we
create workflow diagrams showing how the final song result is produced. We use
these diagrams to clarify the musical idiom related to BassNet outputs
featured in the song.

To present and discuss the pitch content of the audio material, we make use of
Fourier spectra/spectrograms, as well as automatic fundamental pitch detection
using CREPE (Kim et al.,, 2018). In our analysis we explicitly focus on the
fact that when the timbre of an instrument is rich (either by itself or after
effects processing), there is not always a one-to-one relation between nominal
(notated) pitch and perceived pitch, due to the harmonic overtone structure.
To this end we perform manual transcription on some of the audio material. In
these cases we distinguish between two kinds of transcription. The first kind
is a transcription of the perceived pitches. We will refer to this kind of
transcription as _perceptual transcription_. The second kind is a
transcription of the notes that are perceived as being played. We refer to
this kind of transcription as _monophonic transcription_. In the latter kind
the aim is to produce the notes that a bass player would play on their
instrument to produce the audio material to be transcribed, whereas in the
former kind the aim is to notate the pitch sensations that the audio material
produced.

Part of our analysis concerns pitch perception, which is hard to quantify. We
realize that this makes it especially important to provide ample evidence for
the reader to corroborate by themselves. important to provide ample evidence
for the reader to corroborate. To this end we provide a web page with
supplementary figures and audio material at the following URL333In addition to
the page hosted at the URL, the supplementary is published as Grachten,
(2022):

http://aimc2022-suppl-mat.s3-website.eu-west-3.amazonaws.com/index.html

We strongly encourage the reader to consult the supplementary material, which
we will reference frequently in the following sections.

##  4 Results

In this section, we present a selection of results from the collaborative
session between Hyper Music and CSL. Two songs were produced in this session,
titled “Melatonin” and “Skream”, but for brevity we will discuss only the
former. We start with a brief overview of the production strategies employed
by Hyper Music in the session, followed by a description of tweaks to the
sonification process of the AI tool during the session, in response to
suggestions by the artists. After that we analyze the produced material in
detail, highlighting style-related aspects.

###  4.1 Production Strategies

Following (Burgess,, 2013, p.5), we refer to the term “production” as “the
technological extension of composition and orchestration.” Production involves
aspects of the music that can be transcribed as symbolic representation (e.g.
as a score, “composition”), as well as what can generally be considered as
sound (“orchestration”). In the BassNet session the technological extension of
orchestration (mainly by way of BassNet and effects processing) is tightly
interleaved with composition itself, in a process that is commonly labeled
“in-studio composition” (Tamm,, 1995; Deruty et al.,, 2022).

With the term “production strategies”, we refer to the ensemble of creative
processes initiated and followed by the musicians when they build music
content with studio equipment. In the case of the BassNet session, studio
equipment consists of the BassNet tool (in the form of a web interface to a
GPU-powered server running the model), a Digital Audio Workstation with
several commercial audio and instrument plug-ins, and other CSL prototypes
(Supplementary Material A.1).

Hyper Music prepared some audio material ahead of the session, to serve as a
basis for the song. This material was used as conditioning material to produce
outputs with BassNet (Suppl. Mat. A.2). Some of the BassNet outputs that were
selected for inclusion in the song were subsequently used (along with the
other conditioning audio) to produce further BassNet outputs. Most of the
outputs were processed using a variety of effects that are common in studio
production of bass, guitar and other instruments nowadays, including software
emulators of effect-pedals, amplifier, and speaker cabinets.

###  4.2 Tweaks to BassNet sonification

The audio output of BassNet is created using additive synthesis of sine waves,
where at each frame the harmonic series is determined by BassNet’s predicted
fundamental frequency ($f_{0}$) for that frame, and the relative amplitude of
the harmonics is inferred from the predicted CQT spectrum for that frame.
This—rather simplistic—sonification method has a tendency to sound synthetic
an non-expressive, and was cited by Hyper Music as a limitation of the tool.
In response to suggestions by Hyper Music to create a richer sound, changes
were made to the sonification method during the session, providing control
over 1) portamento (continuous transitions between notes) 2) the relative
strength of odd vs even harmonics, and 3) “harmonic variation”. We use the
latter term to refer to a temporal envelope over the harmonics, achieved by a
straight-forward non-linear transformation of the harmonic amplitudes from the
CQT spectral frames (See Suppl. Mat. B). The transformation makes the output
sound more sensitive to details of the conditioning material, and makes
changes of timbre over the course of notes more salient.

The BassNet sounds used in “Melatonin” are shaped using these controls in
accordance with Hyper Music’s preferences: attenuating even harmonics, using
slight portamento at times, and strong harmonic variation.

###  4.3 Relations between Bass Lines – Heterophony

The song (Suppl. Mat. A.3) consists of two main sections, henceforth referred
to as Section 1 and Section 2. A production strategy used extensively in both
sections was the layering of BassNet outputs. The song features a total of 15
single BassNet outputs, grouped together to form composite parts. Figure 1
summarizes the creation/grouping of the individual parts for Section 2 (For
more detailed schemas of both sections with sound examples see Suppl. Mat. A.4
and Suppl. Mat. A.5).

A production strategy used by Hyper Music in Section 1 was an iterative
generation process, where BassNet outputs are conditioned on a mix of the
original material plus BassNet outputs obtained earlier in the process (Suppl.
Mat. A.2). In Section 1 (Suppl. Mat. A.4), the “Bass B” composite part was
generated last, using the groups “Bass A”, and “High pattern” as part of the
conditioning.

Figure 1: Workflow for “Melatonin”, Section 2.

Inside the composite parts, one relation between individual lines is doubling
– see (Moore,, 2016, p. 46). Hyper Music used a pitch-shifter to add a second
line to the original one. Another relation is timbre enrichment. Hyper Music
provided elements of one BassNet output to CSL prototype Notono (Bazin et
al.,, 2021). Using this tool BassNet outputs were transformed by means of
analysis-resynthesis. The transformed outputs are superimposed to the original
output, thus providing additional timbre elements to the original line.

A third relation between individual lines in composite parts is heterophony.
Heterophony is described as the occurrence of “simultaneous variation of a
single melody” (Cooke,, 2001), and elsewhere as “polyphony resulting from
simultaneous differences of pitch produced when two or more people sing or
play more or less the same melodic line at the same time.” (Tagg,, 2014, p.
210).

BassNet can generate different suggestions based on the same audio
conditioning. Given a single conditioning, if two such suggestions derive from
a close enough position in BassNet’s latent space, then the two generated
outputs can be similar to each other yet with clear differences. The
superimposition of such outputs can produce heterophony. Hyper Music used this
production strategy throughout the song. Two examples of this from Section 2
of the song are shown in Figure 2. The first example (top) contains two lines
that are rhythmically distinct, but closely tied in terms of pitch, where
“Bass C1” functions like an embellished variation of “Bass C2”, and thus fits
the definition of heterophony in the above sense. By contrast, the parts “Bass
D1” and “Bass D2” in the second example (bottom) are not obviously variations
of a single melody in terms of pitch contour, and as such are not a typical
example of heterophony. Nevertheless, they are strongly tied rhythmically,
sharing a tendency towards repeated 16th notes.

Both sections of the song make heavy use of the heterophony idiom by
superimposition of bass parts (as witnessed from the video material in Suppl.
Mat. A.4, bottom, and Suppl. Mat. A.5, bottom).

Figure 2: Two transcribed excerpts from Section 2 of “Melatonin” (monophonic
transcription, see Section 3.3). Top: Bars 21 and 22 from parts “Bass C1” and
“Bass C2”. Bottom: Bars 27 and 28 from parts “Bass D1” and “Bass D2”. Sound
excerpts in Suppl. Mat. C.1 and Suppl. Mat. C.2

###  4.4 Homophony _within_ Bass Lines

In the Western classical music tradition, pitch is “[t]he particular quality
of a sound (e.g. an individual musical note) that fixes its position in the
scale” (Haynes and Cooke,, 2001). As such, if the material played by an
instrument is monophonic (consists of a single voice or part), then a single
note—and thus a single pitch—is expected to sound at any given time.

Even if bass parts are commonly conceived as monophonic—evident from the term
“bass line”, often used as a synonym for bass part—it is not always the case
that they convey just a single pitch. In contemporary Popular Music for
example, bass parts may convey multiple simultaneous pitches. They are
typically the result of post-hoc effects-processing or equalization of the
original sound. Three examples of this phenomenon from commercial mixes are
discussed in detail in Suppl. Mat. D.

In Section 4.3 we focused on simultaneous pitches (more specifically
heterophony) as a result of the superimposition of related but different
BassNet outputs. An examination of individual outputs reveals that
simultaneous pitches even occur within a single BassNet output.

As an example of this, Figure 3 shows the Fourier spectrogram of Section 2,
Solo 1, bars 82–83, along with a perceptual transcription of the part. The
transcription was performed by a combination of listening and inspection of
Fourier spectra. To this end the spectra of selected frames (computed with
higher frequency resolution) are plotted and annotated, as shown in Suppl.
Mat. E.1.

Figure 3: Melatonin, section 2, track “Solo 1”, bars 82–83. Top: Fourier
spectrogram; Bottom: Perceptual transcription (see Section 3.3). Corresponding
sound excerpt in Suppl. Mat. E.1

The transcription conveys two simultaneous pitches throughout the part, while
the interval between simultaneous pitches varies over time. The associated
spectra (Suppl. Mat. E.1) generally show attenuated even harmonics, a
consequence of the artists’ preference of odd over even harmonic sonification
in BassNet. Furthermore, the spectra reveal that the temporal variability in
the amplitude of the harmonics, including attenuation of the two lowest
harmonics (as a result of the “harmonic variation” control, see Section 4.2
and Suppl. Mat. B) translates into a variety of modalities for the perception
of pitch. The part has a permanent audible second line, which often follows
harmonic 3 but not all the time.

Similar to the bass line examples from well-known commercial mixes mentioned
above (Suppl. Mat. D), individual BassNet outputs are close to being
polyphonic, where the simultaneous progressions of pitch are tied
rhythmically. In this respect it resembles homorhythmic homophony (Hyer,,
2001), even if the term homophony (just like polyphony in general) is
typically used to denote music where each pitch is produced by a separate
note.

##  5 Discussion

The conditioning material used by Hyper Music consists of various elements,
including voice, chords, and drums. For Section 2 it consists of a simple
acoustic double bass part. The conditioning bass part constrains the
variability of BassNet outputs as a function of the latent space controls,
such that the outputs tend to remain close to the conditioning part. In this
respect Hyper Music used BassNet not so much to come up with a primary idea
for Section 2, as to develop a single part into a Section. The development in
this section consists in a time-varying superimposition of multiple variations
of BassNet outputs, forming rich heterophonic textures (see Section 4.3).

Using Pascall, (2001)’s terms (Section 2), such instances of heterophony may
be considered as an idiom derived from the resources of the performance. The
individual parts that make up the heterophonic texture were created using an
intrinsic affordance of BassNet: its latent space control makes it straight-
forward to create different voices fitting the same conditioning material.

In addition, the individual audio outputs of BassNet also introduce a notable
style, namely the occurrence of simultaneous pitches in a monophonic bass line
(see Section 4.4). This style can also be found in various existing commercial
mixes, where it is typically achieved by production techniques like effects
processing, double tracking (Moore,, 2016, p. 46), and equalization. In
contrast to those techniques, the overtone structure of BassNet outputs is
directly produced as a function of the conditioning audio, inferring harmonic
amplitudes from the predicted CQT spectra.

The permanence of a second perceived pitch value in some outputs is
reminiscent of overtone singing, in which a single vocal performer produces
more than one clearly audible pitch simultaneously (Pegg,, 2001). Although
superficially this seems to be a manifestation of style as characteristic
_sound_ , we have highlighted how the near-polyphonic quality of the sound
constitutes homorhythmic homophony (Hyer,, 2001). As such it can also be
regarded as a musical _idiom_. Note how this blurs the distinction we adopted
from Pascall, (2001) in Section 2 where manifestations of style concern either
the sound, or the idiom.

From the example presented in Section 4.4 we identified the “odd vs even
harmonic” and the “harmonic variation” controls (changes to the BassNet
sonification method prompted by suggestions from the artists, see Section 4.2)
as the source of the latter stylistic characteristics. In particular we found
that the absence of even harmonics, the attenuation of the first two harmonics
and the temporal fluctuation of relative harmonic strengths leads to
ambiguities in pitch perception. The combination of the first two factors
produces a scenario where the lowest component in the tone complex is an odd
harmonic of the original $f_{0}$ and the spacing between harmonics (due to the
attenuation of even harmonics) is $2f_{0}$. This scenario has been shown to
produce two pitch sensations—a phenomenon that poses problems for both
spectral and temporal theories of pitch (Yost,, 2009, pp. 1705–1706). Given
Hyper Music’s concern that the BassNet sonification before the adaptations
tended to produce uninteresting sound qualities, it is conceivable that their
preference for this particular setting is precisely because of the ambiguity
of pitch perception it introduces.

Beyond observations about stylistic aspects of the output, this case study
illustrates some notable points about the use of novel technology for creative
musical purposes in general. First, the additional controls to shape the way
BassNet predictions are sonified were suggested by Hyper Music with an
explicit reference to analog synthesizers as an effective way to interact with
the technology. There are multiple ways to interpret this reference. On a
basic level it may demonstrate the need of music artists to have fine-grained
control of the sounds they produce. But it may also serve as an illustration
that in order for music artists to accept a tool and be able to interact with
it intuitively, its interface must appeal to notions that are familiar to
them. The history of the analog synthesizer itself also holds evidence for
this. For example, the commercial success of the Moog synthesizer—compared to
another type of analog synthesizer introduced around the same time by Don
Buchla—is explained by Pinch and Trocco, (2002) as a result of Bob Moog’s
embrace of the familiar piano keyboard as a way to interact with his
synthesizer (as opposed to Buchla, who rejected it on the grounds that it was
too limited for the possibilities of the synthesizer). In the same vein,
collaborations with musician Herbert Deutsch led Moog to include time-varying
filters that emulate the articulation that is characteristic of conventional
acoustical instruments (Pinch and Trocco,, 2002, page 27).

Second, it can be argued that the non-linear transformation of relative
harmonic strengths of the sound (“harmonic variation”) that turned out to make
the BassNet sound richer and more ambiguous in terms of pitch, is essentially
a rather ad-hoc fix to the synthetic quality of the original sounds, because
the BassNet model was not designed or trained to take this transformation into
account. As such, it is hard to see how this example informs the development
of AI tools for music from a technical perspective. Nevertheless it would be
wrong to negate the role of such serendipity in musical creativity. This role
is clearly illustrated by the works of Schaeffer, (1952) who recognized, for
example, that something as simple as playing back a 78 RPM recording at 33 RPM
can reveal a whole variety of musical forms in the modified audio content that
were not apparent in the original, but may be the basis of a coherent musical
discourse. Therefore, we subscribe to the view—propagated among others by
Sturm et al., (2019); Deruty et al., (2022)—that AI tools for music creativity
should be developed alongside, and in collaboration with musicians.

##  6 Conclusions

In this case study we have investigated style characteristics of a song by
artist duo Hyper Music in relation to BassNet, an AI tool to produce bass
lines that was used extensively in the music production process. For this, we
draw on the definition of style as either sound or musical idiom emerging from
the resources of the performance, in this case BassNet specifically (Pascall,,
2001).

One type of style that is prominent in the song is a heterophony _idiom_ ,
produced by superimposing variations of bass lines. The variations are created
by using the same conditioning material to BassNet with different
configurations of the tool, notably different latent space positions (Grachten
et al.,, 2020).

During the collaborative session between Hyper Music and CSL in which the song
was produced, tweaks were made to the sonification process of BassNet that
resulted in richer and more time-varying timbres. Spectral and perceptual
analysis of BassNet outputs indicates that the resulting harmonic overtone
structure often produces the sensation of two simultaneous pitches originating
from a single note. Although this phenomenon can be regarded as a
manifestation of style through _sound_ , the parallel melodic lines that form
due to the dual pitch percepts effectively form a homorhythmic homophony
musical _idiom_ within a single instrument part. This idiom also occurs in the
bass parts of some commercial contemporary Popular Music—where the effect is
typically produced by double tracking, equalization or other production
strategies—and in overtone singing.

The case study presented here illustrates how AI tools used for music
production—like any other technology or instrument—create affordances that,
depending on the role they play in the music production process, may imbue
musical outputs with a distinct style, both in terms of sound and musical
idiom. Moreover, it shows that bringing AI tools into music practice creates
opportunities for improvements that make the tools more engaging for artists
to work with.

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