The environmental footprint of Generative AI and other Deep Learning

Abstract

Deep learning (DL) technologies is increasing. To understand the scale of the problem and to identify solutions for avoiding excessive energy use in DL research at communities such as the Music Information Retrieva Conference ISMIR, more knowledge is needed of the current energy cost of the undertaken research. In this paper, which is a preview of an upcoming paper co-authored with Anna-Kaisa Kaila and Petra Jääskeläinen, we provide a scoping inquiry of how the ISMIR research concerning automatic music generation (AMG) and computing-heavy music analysis currently discloses information related to environmental impact. We present a study based on two corpora that document 1) ISMIR papers published in the years 2017–2023 that introduce an AMG model, and 2) ISMIR papers from the years 2022–2023 that propose music analysis models and include heavy computations with GPUs. Our study demonstrates a lack of transparency in model training documentation. It provides the first estimates of energy consumption related to model training at ISMIR, as a baseline for making more systematic estimates about the energy footprint of the ISMIR conference in relation to other machine learning events. Furthermore, we map the geographical distribution of generative model contributions and discuss the corporate role in the funding and model choices in this body of work.

Speaker
Andre Holzapfel is Associate Professor of Media Technology with specialization in Sound and Music Computing at KTH Royal Institute of Technology in Stockholm, Sweden. He holds one PhD degree in Computer Science, and a second PhD degree in Ethnomusicology. This multidisciplinary background helps him to investigate the potential of combining quantitative, computational methods with qualitative, ethnographic methods in music research, an investigation that he likes to refer to as Computational Ethnomusicology. In the recent years, he has extended his research to the analysis of human movement using motion capture analysis, and the investigation of ethical, legal, and sustainability implications of artificial intelligence in creative contexts. Despite having a degree in Computer Science he does not like coding. He is a passionate player of the Cretan lute, and used to perform Greek Rembetiko music.

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