Workshop Title

Critical Perspectives on AI/ML in Musical Interfaces

Description

The use of machine learning and AI in everyday applications has taken off in recent years. Now, you can buy a refrigerator with “AI”, but, despite much media interest in “AI composers”, not a musical instrument (or perhaps, not a good one). This workshop seeks to develop a community of NIME researchers and practitioners to analyse the roles that computational intelligence already plays in music technology and where it may play a role in future. 

This workshop follows up on a successful 2020 edition of “Critical Perspectives”. We aim to consolidate current ML-related thought in NIME, and to develop a research network that focuses on future work in ML-enhanced interfaces for musical performance.

Notably, this objective diverges from previous NIME research that has focused on technical implementations; rather, we aim to offer a forum for academic discussions on critical and theoretical perspectives.

Our workshop format is online-first and inclusive in terms of geographic location and timezone as well as background and research area. We aim to include as many participants as possible.

Our workshop will be motivated by three themes, each the focus of one session, and a number of hard questions related to musical AI/ML:

Social and Cultural Impact (session 1):

• What are the potential benefits and drawbacks of using AI/ML in NIMEs?

• Is musical AI/ML a product of our techno-euphoric climate or will its effects be long-lasting?

• What will the effects be on enhancement and reduction of performers' creativity?

• What role could musical AI/ML have in education?

Musicological Perspectives (session 2):

• What are the roles of AI/ML in NIMEs?

• What kind of NIMEs and music technology does AI/ML afford?

• How can AI/ML affect musical practices?

• What distinguishes current/new applications of AI/ML to NIMEs from those already established?

• Can AI/ML-enabled instruments produce unique music?

Diversity and Ethics (session 3):

• What kind of musical bias could data used for training ML models potentially encapsulate?

• How should we cope with legal issues related to data ownership in ML-based NIMEs?

• How should we cope with the environmental issues related to the training of AI/ML for NIMEs?

Each 90-minute session will include two panellists from the NIME community, who will provide a 20-minute orientation talk and discussion on the theme of the session. The rest of the session will involve directed discussions with the participants to develop a body of ML-related research perspectives with an eventual goal of developing an edited volume or journal special issue with contributions from the NIME community.

Short Description

This workshop seeks to develop a community of NIME researchers and practitioners to analyse the roles that computational intelligence already plays in music technology and where it may play a role in future. It follows on the successful 2020 workshop with an inclusive online-first format driven by panel and participant discussions.

We aim to consolidate current ML-related thought in NIME, and to develop a research network that focuses on future work in ML-enhanced interfaces for musical performance. Notably, this objective diverges from previous NIME research that has focused on technical implementations; rather, we aim to offer a forum for academic discussions on critical and theoretical perspectives.

Organizers

Charles Martin (Australian National University)

Charles Martin  is a Lecturer at the Australian National University Research School of Computer Science and was previously a postdoctoral research fellow at the University of Oslo's Robotics and Intelligent Systems Group. His PhD, on designing digital musical instruments to support ensemble improvisation was awarded from the Australian National University in 2016 and he also holds degrees in mathematics and music. Charles is a researcher in music technology, machine learning, and human-computer interaction. His research focus is on how intelligent systems can be deployed and evaluated in complex real-world situations, particularly in the creative arts. His musical works as a percussionist and computer musician have been performed throughout Australia, Europe and the USA and presented at international conferences on music technology and percussion.

Fabio Morreale (University of Auckland)

Fabio Morreale is a Lecturer at the University of Auckland, School of Music. He was previously a postdoctoral research fellow at Queen Mary University of London (Augmented Instruments Lab) and at the University of Trento (interAction Lab). In 2015, he was awarded a PhD in Human-Computer Interaction from the University of Trento (Italy) and he holds a master degree in Computer Science. His research is aimed at critically assessing the impact of technology on music creation, learning, and consumption and at designing counterpowers and alternative futures. 

Benedikte Wallace (University of Oslo)

Benedikte Wallace is a Doctoral Research Fellow at the RITMO Centre for Interdisciplinary Studies of Rhythm, Time and Motion at the University of Oslo.  With a background in music recording and a master degree in Informatics, her academic interests lie in the cross-section between art and science.  Her current research is centred around sound-motion mappings with 3D motion capture, generative machine learning and computational creativity. 

Hugo Scurto (École nationale supérieure des Arts Décoratifs)

Hugo Scurto is a researcher, musician, and designer. He is currently a postdoctoral fellow at EnsadLab, the arts and design research lab of École nationale supérieure des Arts Décoratifs - Paris. He recently completed a PhD in Computer Music at IRCAM, entitled “Designing With Machine Learning for Interactive Music Dispositifs”, under the supervision of Frédéric Bevilacqua. Before that, he spent one year as a visiting researcher at Goldsmiths University of London, working with Rebecca Fiebrink on Human-Centred Machine Learning applied to Music. His research combines qualitative, quantitative, and practice-based methods to study, design, and inquire machine learning technology situated in human musical practices. His practice seeks to create new forms of interactive learning interfaces that critically take into account the creative, cultural, and inclusive dimensions of human musical expression.

Preferred Length of Workshop

We plan to run three 90-minute workshop sessions at different times in during the workshop day to allow participants from all time zones to attend.

This system was highly successful at NIME2020 in allowing a large number of participants to join us throughout the day.

Technical and Space Requirements

Our workshop is planned to be online-first, light in terms of technical requirements and inclusive in terms of participants.

We will publish pre-recorded talks on the workshop website, and plan to use Padlet, and a chat platform for live discussions as well as NIME’s video solution.

Our 2020 edition made heavy use of Discord; however, given that discord is not available in China, we would prefer to use another platform and are open to suggestions from the conference organisers.

Links to Supporting Media

• 2020 edition website: https://critical-ml-music-interfaces.github.io

• Discord forum for 2020 edition: https://discord.gg/cPb69Rh