  Music is a universal phenomenon that profoundly influences human experiences
across cultures. This study investigates whether music can be decoded from
human brain activity measured with functional MRI (fMRI) during its perception.
Leveraging recent advancements in extensive datasets and pre-trained
computational models, we construct mappings between neural data and latent
representations of musical stimuli. Our approach integrates functional and
anatomical alignment techniques to facilitate cross-subject decoding,
addressing the challenges posed by the low temporal resolution and
signal-to-noise ratio (SNR) in fMRI data. Starting from the GTZan fMRI dataset,
where five participants listened to 540 musical stimuli from 10 different
genres while their brain activity was recorded, we used the CLAP (Contrastive
Language-Audio Pretraining) model to extract latent representations of the
musical stimuli and developed voxel-wise encoding models to identify brain
regions responsive to these stimuli. By applying a threshold to the association
between predicted and actual brain activity, we identified specific regions of
interest (ROIs) which can be interpreted as key players in music processing.
Our decoding pipeline, primarily retrieval-based, employs a linear map to
project brain activity to the corresponding CLAP features. This enables us to
predict and retrieve the musical stimuli most similar to those that originated
the fMRI data. Our results demonstrate state-of-the-art identification
accuracy, with our methods significantly outperforming existing approaches. Our
findings suggest that neural-based music retrieval systems could enable
personalized recommendations and therapeutic applications. Future work could
use higher temporal resolution neuroimaging and generative models to improve
decoding accuracy and explore the neural underpinnings of music perception and
emotion.
