The Interpretation Gap in Text-to-Music Generation Models

• URL: http://arxiv.org/abs/2407.10328v1
• Date: Sun, 14 Jul 2024 20:51:08 GMT
• Title: The Interpretation Gap in Text-to-Music Generation Models
• Authors: Yongyi Zang, Yixiao Zhang,
• Abstract summary: We propose a framework to describe the musical interaction process, which includes expression, interpretation, and execution of controls. We argue that the primary gap between existing text-to-music models and musicians lies in the interpretation stage, where models lack the ability to interpret controls from musicians.
• Score: 1.2565093324944228
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large-scale text-to-music generation models have significantly enhanced music creation capabilities, offering unprecedented creative freedom. However, their ability to collaborate effectively with human musicians remains limited. In this paper, we propose a framework to describe the musical interaction process, which includes expression, interpretation, and execution of controls. Following this framework, we argue that the primary gap between existing text-to-music models and musicians lies in the interpretation stage, where models lack the ability to interpret controls from musicians. We also propose two strategies to address this gap and call on the music information retrieval community to tackle the interpretation challenge to improve human-AI musical collaboration.

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