# AI (r)evolution – where are we heading?
Thoughts about the future of music and sound technologies in the era of deep
learning

Authors: Giovanni Bindi1, Nils Demerlé1, Rodrigo Diaz2, David Genova1,
Aliénor Golvet1, Ben Hayes2, Jiawen Huang2, Lele Liu2,
Vincent Martos1, Sarah Nabi1, Teresa Pelinski2, Lenny Renault1,
Saurjya Sarkar2, Pedro Sarmento2, Cyrus Vahidi 2, Lewis Wolstanholme2,
Yixiao Zhang2

Editors: Axel Roebel1, Nick Bryan-Kinns2∗, Jean-Louis Giavitto1, and Mathieu
Barthet2

(1UMR 9912 STMS – IRCAM, CNRS, Sorbonne University, French Ministry of
Culture, Paris, France
2UKRI Centre for Doctoral Training in Artificial Intelligence and Music (AIM)
Queen Mary University of London, UK
)

###### Abstract

Artificial Intelligence (AI) technologies such as deep learning are evolving
very quickly bringing many changes to our everyday lives. To explore the
future impact and potential of AI in the field of music and sound technologies
a doctoral day was held between Queen Mary University of London (QMUL, UK) and
Sciences et Technologies de la Musique et du Son (STMS, France). Prompt
questions about current trends in AI and music were generated by academics
from QMUL and STMS. Students from the two institutions then debated these
questions. This report presents a summary of the student debates on the topics
of: Data, Impact, and the Environment; Responsible Innovation and Creative
Practice; Creativity and Bias; and From Tools to the Singularity. The students
represent the future generation of AI and music researchers. The academics
represent the incumbent establishment. The student debates reported here
capture visions, dreams, concerns, uncertainties, and contentious issues for
the future of AI and music as the establishment is rightfully challenged by
the next generation.

##  1 Introduction

Deep learning-based technologies are evolving very quickly, and seem to become
the basis of numerous changes in our everyday life. However, self-driving
cars, conversational agents, machine-based language translation, and image
generators are only the tip of the iceberg. Machine learning techniques are
developed for a growing list of application domains: medicine, smart cities,
humanoid robots, management of electricity grids, and they are starting to be
used in fundamental research disciplines like physics, chemistry, genetics,
and even mathematics.

Given the apparent proliferation of technologies that enable learning from
data, the Queen Mary University of London (QMUL) & Sciences et Technologies de
la Musique et du Son (STMS) doctoral day held on 13 Feb 2023 aimed to
stimulate discussions about the future impact and potential of Artificial
Intelligence (AI) in the field of music and sound technologies. Eight prompt
questions about current trends in AI and music, and discourse surrounding AI
were generated by academics from QMUL and STMS. Students from the two
institutions then formed teams to discuss and debate these questions. This
report presents a summary of the discussions by research students from QMUL
and STMS. Given the healthy debate around the questions, it must be noted that
the opinions in this text are not necessarily shared by all authors.

##  2 Data, Impact, and the Environment (group A)

### Question 2.1 Machine learning systems rely on large amounts of data that
are difficult and expensive to generate and process. How can we ensure that
academic research remains competitive and innovative compared to the various
industrial players that have access to better computational resources and
larger amounts of data?

Firstly, redundant effort on the same task could be avoided: once there is
enough industrial interest in a specific task, it could be a sign for the
academia to shift their focus towards unsolved research questions. For
example, in the field of source separation, the first breakthroughs happened
with [UPG+17] and U-net [JHM+17] came out in 2017 by Sony and Spotify
respectively, but based on private datasets. After a few years of the SiSEC
(Signal Separation Evaluation Campaign) challenge running and more public
datasets made available did we finally come across an open-source
implementation, Open-Unmix [SULM19], that matched industry model performance
while using the same architecture from [UPG+17]. Subsequently, Deezer released
Spleeter [HKVM20], using a very similar U-net architecture. While the
contribution may be significant in terms of making tools more accessible to a
wider audience, it appears to be limited in terms of methodology. Since then,
the state-of-the-art for music source separation has consistently been pushed
by industry research, such as the latest Hybrid Demucs model [Déf21], which
often relies on smaller contributions from academic researchers to improve
performance. At this point, we see a trend of models trained by academia (such
as LaSAFTNet [CKCJ21]) on publicly available datasets that rarely outperform
state-of-the-art models released by industry giants under the common
evaluation setting. While their contributions may not always lead to major
breakthroughs in the field, they are nevertheless crucial in advancing
research in specific cases uncovered by the industry such as instrument-
specific separation, acappella separation, blind separation, etc.

Secondly, it is crucial to encourage the creation of finished prototypes or
ready-to-use tools in academia, properly licensed to avoid situations where a
company reimplements it and sells it as a commercial product. This is
difficult for some products, such as hardware. And it requires efforts to
maintain a product. Therefore ideally industry and academia could work
together in developing and maintaining a product, through technology transfer,
including licensing and spinout. To this end, academia should recognize
accomplishment in releasing finished prototypes so that student researchers
would be more encouraged to do so.

Thirdly, while huge models like GPTs are trained to solve the general problem
of natural language understanding and generation, academic researchers could
aim at solving specific research questions with smaller models and limited
resources. Academia could take advantage of the large pretrained models from
the industry, and the industry could get insights on improving every single
task. This approach could lead to more efficient use of resources and a better
understanding of how to solve specific research questions.

In conclusion, by avoiding redundant efforts, encouraging creating ready-to-
use tools and technology transfer, and utilizing pre-trained models from the
industry, academia can remain competitive and innovative despite the better
resources and larger datasets available to industrial players.

### Question 2.2 What can be done about the environmental impact of deep
learning approaches?

The increasing use of AI in academic research and the industry is raising
concerns about its environmental impact. Several practices have been suggested
in [SGM19], including reporting training time, sensitivity for machine
learning models, and sharing local infrastructure. A set of efficiency
measures is proposed in [SDSE20], such as carbon emission and electricity
usage.

In this context, there are other initiatives that can be taken to address
these concerns. First, researchers should be encouraged to report the amount
of energy consumption or carbon emissions used in their publications. This
information will help raise awareness of the environmental impact of AI
research, and incentivize researchers to develop more efficient algorithms and
training methods.

Secondly, a task-specific energy rating system can be implemented to label the
energy level of open-source pretrained models. This rating system could be
displayed on open source model hubs such as huggingface
111https://huggingface.co/. By doing so, people can get an idea of the energy
consumption behind each model, and choose a more energy-efficient model for
their specific task.

However, it is important to note that the more efficient a model becomes, the
more it would be used, leading to equal or more energy consumption. This
paradoxical situation is similar to the development of engines. The overall
increase in energy consumption seems to be inevitable.

Back in 2009, there was a debatable article saying that ”Performing two Google
searches from a desk computer can generate about the same amount of carbon
dioxide as boiling a kettle for a cup of tea”
222https://searchengineland.com/calculating-the-carbon-footprint-of-a-google-
search-16105. Another evolution with ChatGPT by Microsoft is happening right
now. Search engines have become a big part of people’s lives, and we cannot
abandon them. Similarly, AI is necessary for the advancement of technology,
but we should be concerned about energy consumption and continue to explore
ways to reduce it.

Meanwhile, we can be optimistic about the use of eco-friendly energy sources
such as solar and wind energy, as well as more energy-efficient computers such
as biological computers becoming more accessible in the near future.

##  3 Responsible Innovation and Creative Practice (group B)

### Question 3.1 What does responsible innovation mean for the field of AI for
sound and music? How should we envision the use of AI in sound processing and
music in a way that artists would find rewarding, and how can we avoid
negative impacts, for example for composers and performers?

In recent years, we have observed a considerable rise in public interest
towards AI generative models. Many discussions have arisen in the media which
question whether image generation tools will eventually ‘replace’ visual
artists, alongside assessing the ethics of using artists’ work to train these
models without their economic compensation [San22, Tho23]. Recent advancements
in audio generation have extended these ethical discussions to the music
industry, where musicians are now also beginning to face very similar issues.

Whilst there are many commonalities between the domains of the visual arts and
music, such as models of remittance/retribution, it appears that the bond
between spectator and artist is generally stronger in popular music than in
the visual arts. With these economic concerns in mind, it is hard to imagine a
fan culture centered around AI generated music, at least in the form we see
today towards our most popular artists. A more immediate issue, however, is
that of AI technologies automating the jobs, such as sound engineering or
mastering, which many musicians undertake to support themselves financially
alongside their composition and performance work. The rate at which new AI
models are released calls for a discussion across disciplines with regards to
how technology’s impact upon these professions may be mitigated. We suggest
that, rather than developing tools that aim to solve a problem (e.g., solve
automatic mixing), we should instead aim to create tools that assist or
support the artist’s work in that particular task [OGWA+23]. Ultimately, the
AI technologies (i.e., the models) would be the same, but instead of being
presented as close-ended generators, they could be developed as interfaces
musicians can interact with and include in their workflows. Even the best
automatic mixing model does not possess the same reflexivity that an expert
mixing engineer has - a sound engineer might have listened to a particular
piece of music earlier on in the day that inspires them to approach mixing in
a new way. Such a dynamicity has not yet been encountered in our current AI
music models, which are yet to adopt such an approach to their materials and
past predictions.

The rigidity of these AI models has further implications in how we define and
understand our musical idioms. Typically, AI technologies are designed to both
codify patterns for use in generative environments and extract dominant
characteristics from arbitrary data points. The prominence of these patterns
and characteristics are typically influenced through a curated training and
evaluation process which, when employed for creative purposes, serve to imbue
some desired semantic/semiotic/qualitative values. In the sound and music
domain, these characteristics are often inferred from the tradition of Western
music theory, particularly functional harmony, metric understandings of
rhythm, standardised instrumental/orchestral forces and the cultural artefacts
that symbolically coincide with them. Although Western music theory has it
uses, and is both studied and understood by many, its effectiveness in both
practice and technological contexts is intrinsically limited. In many cases,
music theory does not even apply to many Western music contexts, and it also
struggles to remain relevant when extended to other cultural traditions (see
e.g. [Aga92]). As a result of this, practitioners tend to have a dynamic
relationship towards their understanding of music theory, employing it
instinctively, and defying it or redefining it on a regular basis. These
limitations of Western music theory are similarly transferred to the
technologies that robustly and naively center around them, which detracts from
our more general cultural understanding as much as it limits the applicability
and scope of these technologies.

Moreover, the development of AI technology, as it is currently organised,
leaves little place for critical discussion on ‘why’ and ‘how’ our AI tools
are created outside of a race for scientific progress. AI technologies are too
often immediately deployed and shared as functional and effective tools
without concern for the ethical and sociotechnical questions that the concept
of a ‘tool’ entails. This is heightened by the frequent use of analogies
between neural networks and biological processes in both research and
pedagogical contexts (e.g. the analogy between the brain neuron and the neuron
from a neural network). This drives a narrative in which AI technologies are
‘naturalised’, giving them a sort of autonomy, as if AI researchers and
developers’ tasks were to discover or to unearth the natural processes behind
the ‘already existing’ AI and deep learning technologies, akin to studying
animal or human intelligence. Instead, it must be stated that these
technologies are human-made, and that society, and (especially) researchers
and programmers, hold responsibility for how and why they are created, the
biases they contain and the position they occupy within our culture.

A de-naturalisation or de-mystification of AI would contribute to refocusing
responsibility towards the researchers and developers that build these
technologies. That being said, the impact of these technologies is not
foreseeable from the perspective of a single discipline. Responsibility is
achieved through interdisciplinary research narratives - a responsible
innovation takes care over all disciplines which relate to the specific topic
of study. What Donna Haraway termed “response-ability” [Har16], Debaise &
Stengers describe as “the capacity to be accountable for an action or an idea
to those for whom the action or idea will have consequences” [DS17, p.17].
Response-ability, in this sense, is the desire for technological research and
culture to work together to preserve and strengthen their interdependence. It
encourages them to come “face-to-face” [Har03] with one another, to be able to
empathise with one another, and to remain considerate and explicit with
regards to the power that one might have over the other. As technological and
cultural landscapes evolve sympathetically, it is the responsibility of those
who forge them to engender and maintain their togetherness.

### Question 3.2 Currently, most research activities dealing with AI for sound
and music focus around generation, analysis, and synthesis. Can we imagine AI
contributions to music playing, music listening, musical performances,
manufacturing of (augmented) instruments, acoustic contexts such as concert
halls, the diffusion or design of sound, or other domains not yet considered?

Mainstream media, industries and publications tend to gravitate towards AI
models that focus upon tasks such as music generation conditioned on artists
or genres (e.g., Jukebox [DJP+20]) or, more recently, text-to-music generation
(e.g., MusicLM [ADB+23a]). These tasks are highlighted and celebrated for
their impressive coverage and holistic scope, whilst the achievements of many
smaller AI technologies are often easy to understate and overlook. More
situated, ambiguous or highly contextualised uses of AI, such as augmented
instrument design, do not receive the same mainstream attention, despite their
own ingenuity and prowess. The more specific an application of AI becomes, or
the more situated it becomes within a particular art practice, the more likely
it is to occupy a niche position in the overall conversation surrounding AI
for sound and music.

The prevalent tasks in sound and music generation have also carved out a
particular space in academia, encouraging the formation of standardised
benchmarks, the establishment of objective evaluation metrics, and the
creation of publications which focus solely on the evaluation of these AI
models [KZRS19]. Tasks that are harder to evaluate or standardise have greater
difficulty entering into the academic publication schema with the same status.
As these larger generative tasks are typically more open–ended and relatable,
there is much more opportunity for others to become influenced by these
models, and continually develop upon them. This encourages research to fall
into distinct trends, where technologies linearly influence the development of
one another, and tasks become more generalised as they develop. As a result,
agreed upon quantitative standards arise to compare the multitude of
approaches towards a given task, and the breadth and scope of these
evaluations is effectively narrowed. This progress narrative, akin to what
Thomas Kuhn describes as “normal science” [Kuh96], produces the perception
that everybody is working on the same thing, and marginalises those who are
working outside of these dominant approaches.

As the development and use cases of these mainstream technologies become more
generalised, they too begin to have subversive effects on cultural practice
and its related industries. Technological pursuits which may have originated
from ideas relating to the creation of sounds in specific contexts, have now
matured and become generalised synthesis tools, equally applicable to a wide
range of scenarios. And as these tools become more widely applicable, they
also obfuscate the need for more context-dependent devices, as well as
techniques for some of our more underused and unique musical practices and
idioms. In line with this idea of ‘normal science’, we can think of these
technologies as encouraging a sense of ‘normal practice’, whereby cultural
development is fixated on perfecting some major aspect of its activity, as
opposed to encouraging experimentalism and the accumulation of new practice
driven techniques. As far as the grander narrative of computer music research
is concerned, this is a distinct shift from the practices of our previous
cultures of research - those whom, like Jean Claude-Risset and Miller
Puckette, engaged in their research as means of furthering both their own
creative practice and the landscape of potential creative techniques and ideas
[Ris03].

In terms of responsible approaches towards AI, and the development of
technologies that are successful outside the dominant trends of generative
music, synthesis and analysis, it is important to remain situated within and
connected to exploratory and experimental arts practices. As an incentive for
research, many of the fringe aesthetics involved in these cultures incite the
curation of new directives and techniques for creative and technological
expression. Although these practices generally fall outside of the mainstream,
and similarly will not receive the same media and industrial attention as the
larger AI models do, they are just as important for the development of
technology as they are for the continued growth of our cultural narrative and
understandings. Where the ethical quandaries towards artists and their
practices are concerned, research that pertains a strong relationship with the
arts and its development aligns itself with Haraway’s aforementioned idea of
“response-ability” [Har16]. In this sense, technological development and
research may embody the same sense of creativity and inventiveness as the
cultural practices it works alongside. And in doing so, in supporting these
more situated affinities, the overarching narrative of progress is mitigated
and directed away from a culture of normal practice and obfuscation, and
towards one where artists, musicians and technologists can continue to prosper
together, in sympathy and interdependently.

##  4 Creativity and Bias (group C)

### Question 4.1 AI generators like DALL·E 2, or MusicLM produce media content
based on text prompts, but can we call these systems creative? What is
creativity and what are the characteristics of an activity we would want to
call creative? To what extent can we expect to find these characteristics in a
DNN?

Prior to assessing the creative potential of deep generative models such as
DALL·E [RDN+22] or MusicLM [ADB+23b], one should discuss the core concept of
creativity itself, which is complex and challenging to define precisely. Among
the many attempts to define creativity, one can identify recurrent properties
which are shared by the majority of literature on the topic, namely the ideas
of novelty, intention and cultural relevance [ED20]. Creative acts are
purposeful and deliberate, involving the author’s intent and choices, which is
fundamentally different from the process of imitation. As current deep
generative models like DALL·E and MusicLM are trained to extract statistical
patterns from the training data, they lack the required intentionality to
pursue a true creative act. Furthermore, the authors in [PC18] argue that,
even in a scenario where machines would develop a sense of volition/intention,
thus being able to create something uninfluenced by human-made art, it would
not be possible for us to understand their creative outcome, for we are bound
to frame such outcome from a human perspective.

Additionally, we suggest that the best we can aim at, as human beings standing
by an artistic creation by another entity, is to an understanding of what
could have motivated another human being to create such a work.

These systems can also be intended as proposal generators, which can be
refined by the end user. One interesting aspect is that many artists
deliberately deviate from the original objective of such models by feeding
them unexpected inputs. To this extent, they seek to break the initial system
in order to fully leverage all the expressive abilities of these models
[YKM21].

In the end, we believe that generative systems should not be aimed at
replacing humans but rather as offering co-creative tools [ED20] which could
complement and extend current instruments and they should be designed to
reflect the will to pursue artistic intentions.

### Question 4.2 How can we reduce the bias in machine learning models? E.g.
bias towards Western musical cultures?

There are many aspects to be considered when reflecting on biases in machine
learning models. It is important to recognize that we cannot completely unbias
the output of a generative model, since there will always be an unremovable
source of bias coming from the data used to train the system on. Still,
cultural biases can represent a discriminating factor towards under-
represented scientific and artistic communities. When attempting to reduce
such components, encouraging diversity should be the main goal. To this end,
we believe that it is important to reinforce the need for datasets that target
culturally different segments. The creation of special conference tracks or
journal special issues encouraging the production and release of datasets that
are culturally diverse (e.g. International Society for Music Information
Retrieval (ISMIR)) can help stimulating the development of models that are not
biased towards, e.g., Western musical cultures, bringing together researchers
and practitioners from different backgrounds and cultures.

It is also important to recognize that biases can sometimes be task-oriented
or useful, especially in user-oriented deep learning products where biases
from a user perspective may be desirable. For example, a recommendation system
for a particular genre of music may be biased towards that genre, but it may
also be useful for users who are looking for music in that genre. It is
essential to evaluate biases on a case-by-case basis to determine whether they
are helpful or detrimental.

Finally, introducing the correct inductive biases in deep models can help ease
down the learning process and reduce the number of training examples required
[GB22]. It is thus important to carefully differentiate whether bias can
potentially be harmful, e.g. coming from a strongly biased dataset, or useful,
as in the case where modelling choices can enhance the learning capabilities
of such artificial agents.

##  5 From Tools to the Singularity (group D)

### Question 5.1 Is the singularity around the next corner or are there
implicit limitations to AI systems that will keep them “dumb” irrespective of
the amount of data we train them with?

The concept of singularity - a point in time when artificial intelligence
surpasses human intelligence and becomes self-improving [EMSS12] \- has been
the subject of much speculation and debate. In our discussion, we explored the
question of whether the singularity is around the next corner or if there are
inherent limitations that will keep AI systems “dumb” regardless of the amount
of data used to train them.

While we did not arrive at a definitive answer, we acknowledged that there
have been numerous breakthroughs in AI recently that are becoming more
frequent. Early in 2017, R-net [WYW+17] has demonstrated performance beyond
the level of human experts in the Question Answering task; Following the
theory of scaling law [KMH+20], recent large models containing massive
parameters and trained with massive data have indeed shown better performance
than previous AI models on various tasks [KCK+23].

There are many challenges and limitations that AI systems face, even with
large amounts of data. The first thing that should be considered is the
potential ethical issues. For instance, there are concerns around data
privacy, stereotypes, and the ethical implications of AI-driven decision-
making [BGBM21, KB21, HJ20]. These issues are already being manifested in a
wide range of AI applications and have been embodied as never before with the
development of large-scale models, from predictive policing and facial
recognition technology [KM19], to disputes over copyright ownership [MAT+21].

In this context, the intelligence exhibited by AI systems may be criticized as
”dumb” since it cannot guarantee responsible output. Given the rapidly
evolving nature of these technologies, it is essential for humans to consider
the ethical implications of AI systems and engage in ongoing dialogues to best
manage their associated risks and benefits. However, it is challenging to
address this issue fundamentally due to the neural network structure of large
AI models, which often lack interpretability [XUD+19]. Despite this challenge,
it is crucial to prioritize transparency, accountability, and openness in AI
development and engage with a broad range of stakeholders, including
government regulators, civil society organizations, and affected communities.

Overall, our discussion implies that despite the potential for large models to
surpass human performance and reach the singularity, they may still exhibit
“dumb” aspects that need to be addressed. Thus, it is crucial to focus on the
more immediate and pressing concerns related to AI applications to ensure that
these technologies are developed and deployed in a manner that aligns with our
values and promotes the greater good.

### Question 5.2 Do AI systems escape the notion of being tools? Applied to
the field of sound and music, are they qualitatively different from digital
audio tools? Or is their contribution limited to that of a tool in the hands
of artists or listeners, where these new AI-based techniques simply allow
doing things better/differently?

To answer the question of whether AI systems in sound and music can be seen as
more than just tools, it is crucial to first distinguish between a tool and an
agent and provide some definitions. Generally, from a philosophical point of
view, one could ask whether AI systems have agency, autonomy, intentionality,
consciousness, or moral responsibility [Lis21]; from a practical point of
view, one could ask whether AI systems can perform tasks that are beyond the
capabilities or expectations of human users, or whether they can influence or
interact with human users in ways that are not predetermined by their design.
In the field of sound and music, we should consider how AI systems affect the
roles and relationships between composers, performers, listeners, and critics.

While music AI systems still operate within the parameters set by their
programmers and users, they do have the ability to introduce new levels of
surprise, unpredictability, and creativity into the creative process:

  1. 1.

One example of this is the creation of AI-generated music [BHP17], where AI
systems can generate original pieces of music based on existing data or
specifications. This can be seen as going beyond the role of a tool and into
the realm of a creative collaborator or even composer. However, while AI-
generated music has been around for at least 20 to 40 years, there has yet to
be a sizable community of people coalescing around it, unlike the case with
the development of new musical instruments or algorithms in the past.

  2. 2.

Another example is the use of AI for sound design [ZWF+18], where AI systems
can be used to generate or manipulate sounds in ways that would be difficult
or impossible for a human to do manually. This can be seen as expanding the
creative possibilities of sound design beyond what traditional tools allow.

AI is also playing a larger role in shaping how people consume music through
the rise of AI-generated playlists [FKL+21] and personalized music
recommendations [FPZ+19]. This can be seen as going beyond the role of a tool
in the hands of listeners and into the realm of a new type of music curator or
even a tastemaker.

However, there are still valid arguments for viewing AI systems in sound and
music as simply tools that assist artists and listeners in achieving their
goals. It is important to consider the limitations and challenges of these
technologies in creating truly surprising and innovative works. Overall, the
potential for AI to develop intentionality and generate new forms of art is
intriguing, but it also highlights the need for ongoing discussion and
exploration in this field.

## Acknowledgments

The doctoral day was supported by Sciences et Technologies de la Musique et du
Son (STMS, France) and by the UKRI Centre for Doctoral Training in Artificial
Intelligence and Music, supported jointly by UK Research and Innovation [grant
number EP/S022694/1] and Queen Mary University of London (QMUL, UK).

∗Nick Bryan-Kinns is now at the Creative Computing Institute, University of
the Arts London.

## References

  * [ADB+23a] Andrea Agostinelli, Timo I Denk, Zalán Borsos, Jesse Engel, Mauro Verzetti, Antoine Caillon, Qingqing Huang, Aren Jansen, Adam Roberts, Marco Tagliasacchi, et al.  MusicLM: Generating music from text.  arXiv preprint arXiv:2301.11325, 2023.
  * [ADB+23b] Andrea Agostinelli, Timo I. Denk, Zalán Borsos, Jesse Engel, Mauro Verzetti, Antoine Caillon, Qingqing Huang, Aren Jansen, Adam Roberts, Marco Tagliasacchi, Matt Sharifi, Neil Zeghidour, and Christian Frank.  Musiclm: Generating music from text, 2023.
  * [Aga92] Kofi Agawu.  Representing african music.  Critical Inquiry, 18(2):245–266, 1992.
  * [BGBM21] Rishi Bommasani, Timnit Gebru, Emily M Bender, and Margaret Mitchell.  Ethical and social risks of harm from language models.  arXiv preprint arXiv:2112.04359, 2021.
  * [BHP17] Jean-Pierre Briot, Gaëtan Hadjeres, and François-David Pachet.  Deep learning techniques for music generation–a survey.  arXiv preprint arXiv:1709.01620, 2017.
  * [CKCJ21] Woosung Choi, Minseok Kim, Jaehwa Chung, and Soonyoung Jung.  Lasaft: Latent source attentive frequency transformation for conditioned source separation.  In ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 171–175. IEEE, 2021.
  * [Déf21] Alexandre Défossez.  Hybrid spectrogram and waveform source separation.  arXiv preprint arXiv:2111.03600, 2021.
  * [DJP+20] Prafulla Dhariwal, Heewoo Jun, Christine Payne, Jong Wook Kim, Alec Radford, and Ilya Sutskever.  Jukebox: A generative model for music.  arXiv preprint arXiv:2005.00341, 2020.
  * [DS17] Didier Debaise and Isabelle Stengers.  The insistence of possibles: Towards a speculative pragmatism.  Parse Journal, (7):13–19, 2017.
  * [ED20] Philippe Esling and Ninon Devis.  Creativity in the era of artificial intelligence.  In Journées d’Informatique Musicale, Strasbourg, France, October 2020.  Keynote paper - JIM Conference 2020 - 12 pages.
  * [EMSS12] Amnon H Eden, James H Moor, Johnny H Søraker, and Eric Steinhart.  Singularity hypotheses.  The Frontiers Collection. Springer, Berlin, 2012.
  * [FKL+21] Andres Ferraro, Yuntae Kim, Soohyeon Lee, Biho Kim, Namjun Jo, Semi Lim, Suyon Lim, Jungtaek Jang, Sehwan Kim, Xavier Serra, et al.  Melon playlist dataset: A public dataset for audio-based playlist generation and music tagging.  In ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 536–540. IEEE, 2021.
  * [FPZ+19] Ferdos Fessahaye, Luis Perez, Tiffany Zhan, Raymond Zhang, Calais Fossier, Robyn Markarian, Carter Chiu, Justin Zhan, Laxmi Gewali, and Paul Oh.  T-recsys: A novel music recommendation system using deep learning.  In 2019 IEEE international conference on consumer electronics (ICCE), pages 1–6. IEEE, 2019.
  * [GB22] Anirudh Goyal and Yoshua Bengio.  Inductive biases for deep learning of higher-level cognition.  Proceedings of the Royal Society A, 478(2266):20210068, 2022.
  * [Har03] Donna Jeanne Haraway.  The Companion Species Manifesto: Dogs, People, and Significant Otherness.  Prickly Paradigm Press, Chicago, IL, 2003.
  * [Har16] Donna Jeanne Haraway.  Staying with the Trouble: Making Kin in the Chthulucene.  Experimental Futures: Technological Lives, Scientific Arts, Anthropological Voices. Duke University Press, Durham, NC, 2016.
  * [HJ20] Dirk Hovy and Anders Trærup Johannsen.  Stereotyping norwegian salmon: An inventory of pitfalls in fairness benchmark datasets for natural language processing.  In Proceedings of the Third Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda, pages 80–90, 2020.
  * [HKVM20] Romain Hennequin, Anis Khlif, Felix Voituret, and Manuel Moussallam.  Spleeter: a fast and efficient music source separation tool with pre-trained models.  Journal of Open Source Software, 5(50):2154, 2020.  Deezer Research.
  * [JHM+17] Andreas Jansson, Eric J. Humphrey, Nicola Montecchio, Rachel M. Bittner, Aparna Kumar, and Tillman Weyde.  Singing voice separation with deep u-net convolutional networks.  In Sally Jo Cunningham, Zhiyao Duan, Xiao Hu, and Douglas Turnbull, editors, Proceedings of the 18th International Society for Music Information Retrieval Conference, ISMIR 2017, Suzhou, China, October 23-27, 2017, pages 745–751, 2017.
  * [KB21] Vitalii Kolesnyk and Marco Baroni.  Large pre-trained language models contain human-like biases of what is right and wrong to do.  arXiv preprint arXiv:2103.11790, 2021.
  * [KCK+23] Jan Kocoń, Igor Cichecki, Oliwier Kaszyca, Mateusz Kochanek, Dominika Szydło, Joanna Baran, Julita Bielaniewicz, Marcin Gruza, Arkadiusz Janz, Kamil Kanclerz, et al.  ChatGPT: Jack of all trades, master of none.  arXiv preprint arXiv:2302.10724, 2023.
  * [KM19] Rob Kitchin and Sophia Maalsen.  Ethical, legal and social challenges of predictive policing.  SSRN Electronic Journal, 2019.
  * [KMH+20] Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei.  Scaling laws for neural language models.  arXiv preprint arXiv:2001.08361, 2020.
  * [Kuh96] Thomas S. Kuhn.  The Structure of Scientific Revolutions.  University of Chicago Press, Chicago, IL, third edition, 1996.
  * [KZRS19] Kevin Kilgour, Mauricio Zuluaga, Dominik Roblek, and Matthew Sharifi.  Fréchet audio distance: A metric for evaluating music enhancement algorithms.  In 20th Annual Conference of the International Speech Communication Association, Graz, Austria, 2019.
  * [Lis21] Christian List.  Group agency and artificial intelligence.  Philosophy & technology, 34(4):1213–1242, 2021.
  * [MAT+21] Brent Daniel Mittelstadt, Patrick Allo, Mariarosaria Taddeo, Sandra Wachter, and Luciano Floridi.  The ethics of algorithms: key problems and solutions.  AI & SOCIETY, pages 1–20, 2021.
  * [OGWA+23] Ozlem Ozmen Garibay, Brent Winslow, Salvatore Andolina, Margherita Antona, Anja Bodenschatz, Constantinos Coursaris, Gregory Falco, Stephen M. Fiore, Ivan Garibay, Keri Grieman, John C. Havens, Marina Jirotka, Hernisa Kacorri, Waldemar Karwowski, Joe Kider, Joseph Konstan, Sean Koon, Monica Lopez-Gonzalez, Iliana Maifeld-Carucci, Sean McGregor, Gavriel Salvendy, Ben Shneiderman, Constantine Stephanidis, Christina Strobel, Carolyn Ten Holter, and Wei Xu.  Six human-centered artificial intelligence grand challenges.  International Journal of Human–Computer Interaction, 39(3):391–437, 2023.
  * [PC18] Rui Penha and Miguel Carvalhais.  Will Machinic Art Lay Beyond Our Ability to Understand It?  In Proceedings of the 24th International Symposium on Electronic Art, pages 22–30, 2018.
  * [RDN+22] Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen.  Hierarchical text-conditional image generation with clip latents.  arXiv preprint arXiv:2204.06125, 2022.
  * [Ris03] Jean-Claude Risset.  Computer music: Why?  Technical report, France-University of Texas Institute, 2003.
  * [San22] Romano Santos.  Can AI-Generated Art Replace Creative Humans?  Vice, November 2022.
  * [SDSE20] Roy Schwartz, Jesse Dodge, Noah A Smith, and Oren Etzioni.  Green ai.  Communications of the ACM, 63(12):54–63, 2020.
  * [SGM19] Emma Strubell, Ananya Ganesh, and Andrew McCallum.  Energy and policy considerations for deep learning in nlp.  arXiv preprint arXiv:1906.02243, 2019.
  * [SULM19] Fabian-Robert Stöter, Stefan Uhlich, Antoine Liutkus, and Yuki Mitsufuji.  Open-unmix-a reference implementation for music source separation.  Journal of Open Source Software, 4(41):1667, 2019.
  * [Tho23] Matthew Thompson.  AI art generators face backlash from artists - but could they unlock creative potential?  Sky News, April 2023.
  * [UPG+17] Stefan Uhlich, Marcello Porcu, Franck Giron, Michael Enenkl, Thomas Kemp, Naoya Takahashi, and Yuki Mitsufuji.  Improving music source separation based on deep neural networks through data augmentation and network blending.  In 2017 IEEE international conference on acoustics, speech and signal processing (ICASSP), pages 261–265. IEEE, 2017.
  * [WYW+17] Wenhui Wang, Nan Yang, Furu Wei, Baobao Chang, and Ming Zhou.  R-net: Machine reading comprehension with self-matching networks.  In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 189–198, 2017.
  * [XUD+19] Feiyu Xu, Hans Uszkoreit, Yangzhou Du, Wei Fan, Dongyan Zhao, and Jun Zhu.  Explainable AI: A brief survey on history, research areas, approaches and challenges.  In Natural Language Processing and Chinese Computing: 8th CCF International Conference, NLPCC 2019, Dunhuang, China, October 9–14, 2019, Proceedings, Part II 8, pages 563–574. Springer, 2019.
  * [YKM21] Matthew Yee-King and Louis McCallum.  Studio report: sound synthesis with ddsp and network bending techniques.  July 2021.
  * [ZWF+18] Yipin Zhou, Zhaowen Wang, Chen Fang, Trung Bui, and Tamara L Berg.  Visual to sound: Generating natural sound for videos in the wild.  In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3550–3558, 2018.