Brain2Music: Reconstructing Music from Human Brain Activity
Timo I. Denk Yu Takagi Takuya Matsuyama Andrea Agostinelli Tomoya Nakai Christian Frank Shinji Nishimoto
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