# Brains on Beats

arXiv: https://arxiv.org/abs/1606.02627 · HTML: https://mlsanity.com/paper/1606.02627/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2016.

## Built on by (most cited first, 4 of 4)

- [MuseChat: A Conversational Music Recommendation System for Videos](https://mlsanity.com/paper/2310.06282/) (2023)
- [Brain2Music: Reconstructing Music from Human Brain Activity](https://mlsanity.com/paper/2307.11078/) (2023)
- [Reverse the auditory processing pathway: Coarse-to-fine audio reconstruction from fMRI](https://mlsanity.com/paper/2405.18726/) (2024)
- [Better audio representations are more brain-like: linking model-brain alignment with performance in downstream auditory tasks](https://mlsanity.com/paper/2511.16849/) (2025)

## Abstract (arXiv)

We developed task-optimized deep neural networks (DNNs) that achieved state-of-the-art performance in different evaluation scenarios for automatic music tagging. These DNNs were subsequently used to probe the neural representations of music. Representational similarity analysis revealed the existence of a representational gradient across the superior temporal gyrus (STG). Anterior STG was shown to be more sensitive to low-level stimulus features encoded in shallow DNN layers whereas posterior STG was shown to be more sensitive to high-level stimulus features encoded in deep DNN layers.