UNSUPERVISED 2021 Shortlisted for The Digital City Awards 2022
The Machine Learning for Music (ML4M) Working Group recently got shortlisted for the Digital City Awards (Best Use of Technology – Not-for-Profit category) for their debut project UNSUPERVISED 2021.
14 February 2022
The Digital City Awards recognise and reward the organisations, teams, and individuals who are helping to build a better future through technology. The complete shortlist for this year’s awards can be found here.
The winners will be revealed at a live awards ceremony on 10 March 2022 at the Etihad Stadium (Manchester City FC), hosted by the chart-topping singer-presenter Sheila Gordhan.
The festival will take place in Manchester, 7-11 March 2022. For a full festival schedule and registration info, visit here.
ML4M is an interdisciplinary community of artists, scientists and engineers. Bringing together postgraduate researchers at RNCM, the University of Manchester, as well as other Manchester-based guest artists, the group is focused upon exploring the creative use of emerging Artificial Intelligence (AI) and Machine Learning (ML) technologies.
ML4M was established in 2020 by Dr Sam Salem (PRiSM Lecturer in Composition, RNCM), Professor Ricardo Climent (Professor of Interactive Music Composition at the University of Manchester), and Dr Richard Allmendinger (Associate Professor in Decision Sciences, Alliance Manchester Business School).
We are delighted to have been nominated for the Digital City Awards!
This project, an exciting interdisiciplinary collaboration between PRiSM, the Novars Research Centre and the Alliance Manchester Business School, brings together postgraduate composers, artists and computer science / engineering specialists. The Machine Learning for Music Working Group is still very much in its infancy, having been established in 2020, and it is therefore hugely encouraging to receive this nomination for our first event! We look forward to presenting our latest work in the next Unsupervised events, scheduled for June 2022.
Dr Sam Salem, PRiSM Lecturer in Composition
It is a superb success to be shortlisted for the “Best Use of Technology – Not-for-Profit” category at this year’s Digital City Awards. Since its foundation more than 1 year ago, the team has been working very hard to get to this point. What I like most about Unsupervised is the interdisciplinary nature of the team: Composers, musicians, audiovisual artists, and machine learning scientists work together to get the most out of cutting-edge technologies and AI algorithms to create the music of tomorrow.
Dr Richard Allmendinger, Associate Professor in Decision Sciences, Alliance Manchester Business School
About UNSUPERVISED 2021
One of the headline events for PRiSM Future Music #3 Festival in June 2021, the UNSUPERVISED 2021 project featured 8 new works engaging creatively with Machine Learning technology, created by 8 RNCM PRiSM and University of Manchester doctoral researchers / artists Anastasios Asonitis, Vicky Clarke, Hongshuo Fan, Robert Laidlow, Zakiya Leeming, Tywi Roberts, Chris Rhodes, and Ellen Sargen (whose piece You May Own Us But We Are Going To Inform On You will be performed again by clarinettist Sarah Watts at the Firth Hall in Sheffield on 31 March 2022, ticket info here).
Among the UNSUPERVISED 2021 line-up, 5 pieces were created using RNCM PRiSM’s flagship software tool, PRiSM SampleRNN, and were direct results of collaborations between the students and Dr Christopher Melen – PRiSM Research Software Engineer.
My compositional process sees me working in all kinds of collaborations with performers to create new pieces, often creating a space to learn from each other and reflect this in our work. In this project, the ML4M group became an extended community of collaborators for me, a rich resource in which to share knowledge, ideas and reflect on AI behaviours. I look forward to seeing how these relationships will develop in future pieces.
Ellen Sargen, RNCM PRiSM Doctoral Researcher
Unsupervised was a great opportunity to discover what ML can offer a creative practice, and working with the ML4M group has been a wonderful way to explore this new technology as a community.
Zakiya Leeming, RNCM PRiSM Doctoral Researcher
Working alongside such a talented and diverse group of artists and specialists; comparing notes over a whole academic year; sharing each other’s successes and failure during experimentation; and seeing the final works come together has been one of the highlights of my PhD programme. The support from PRiSM for working with sampleRNN was just one of the aspects which opened up whole new creative avenues for me.
Tywi Roberts, RNCM PRiSM Doctoral Researcher
The success of ‘Unsupervised’ is based on the fact that the composers involved come from different backgrounds and yet we all attempt to understand this new technology in musical and philosophical terms.
Anastasios Asonitis, Doctoral Researcher, NOVARS Research Centre, the University of Manchester
Being a part of the Unsupervised team, since its inception, has been an incredible and rewarding experience. Working in a collective, focused on the application of AI for musical creativity, is always inspiring; as curious members, we help each other think about how AI can be applied to music composition by describing our philosophy when applying AI to art, our methods (technical and aesthetic) and our artistic angles.
Chris Rhodes, Doctoral Researcher, NOVARS Research Centre, the University of Manchester