Challenges in creative generative models for music: a divergence maximization perspective

• URL: http://arxiv.org/abs/2211.08856v1
• Date: Wed, 16 Nov 2022 12:02:43 GMT
• Title: Challenges in creative generative models for music: a divergence maximization perspective
• Authors: Axel Chemla--Romeu-Santos, Philippe Esling
• Abstract summary: Development of generative Machine Learning models in creative practices is raising more interest among artists, practitioners and performers. Most models are still unable to generate content that lay outside of the domain defined by the training dataset. We propose an alternative prospective framework, starting from a new general formulation of ML objectives.
• Score: 3.655021726150369
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The development of generative Machine Learning (ML) models in creative practices, enabled by the recent improvements in usability and availability of pre-trained models, is raising more and more interest among artists, practitioners and performers. Yet, the introduction of such techniques in artistic domains also revealed multiple limitations that escape current evaluation methods used by scientists. Notably, most models are still unable to generate content that lay outside of the domain defined by the training dataset. In this paper, we propose an alternative prospective framework, starting from a new general formulation of ML objectives, that we derive to delineate possible implications and solutions that already exist in the ML literature (notably for the audio and musical domain). We also discuss existing relations between generative models and computational creativity and how our framework could help address the lack of creativity in existing models.

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