Brain2Music: Reconstructing Music from Human Brain Activity

Autor:	Denk, Timo I., Takagi, Yu, Matsuyama, Takuya, Agostinelli, Andrea, Nakai, Tomoya, Frank, Christian, Nishimoto, Shinji
Rok vydání:	2023
Předmět:	Quantitative Biology - Neurons and Cognition Computer Science - Machine Learning Computer Science - Sound Electrical Engineering and Systems Science - Audio and Speech Processing
Druh dokumentu:	Working Paper
Popis:	The process of reconstructing experiences from human brain activity offers a unique lens into how the brain interprets and represents the world. In this paper, we introduce a method for reconstructing music from brain activity, captured using functional magnetic resonance imaging (fMRI). Our approach uses either music retrieval or the MusicLM music generation model conditioned on embeddings derived from fMRI data. The generated music resembles the musical stimuli that human subjects experienced, with respect to semantic properties like genre, instrumentation, and mood. We investigate the relationship between different components of MusicLM and brain activity through a voxel-wise encoding modeling analysis. Furthermore, we discuss which brain regions represent information derived from purely textual descriptions of music stimuli. We provide supplementary material including examples of the reconstructed music at https://google-research.github.io/seanet/brain2music Comment: Preprint; 21 pages; supplementary material: https://google-research.github.io/seanet/brain2music
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2307.11078 Zobrazit plný text záznamu View this record from Arxiv