Brains on Beats
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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.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 8 Jun 2016.
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Citing papers per year: 2023: 2; 2024: 1; 2025: 1.
4 papers.
- 2023MuseChat: A Conversational Music Recommendation System for Videoscited by 21
- 2023Brain2Music: Reconstructing Music from Human Brain Activitycited by 10
- 2024Reverse the auditory processing pathway: Coarse-to-fine audio reconstruction from fMRIcited by 2
- 2025Better audio representations are more brain-like: linking model-brain alignment with performance in downstream auditory taskscited by 0
- 2025Better audio representations are more brain-like: linking model-brain alignment with performance in downstream auditory tasks20 Nov 2025
- 2024Reverse the auditory processing pathway: Coarse-to-fine audio reconstruction from fMRI29 May 2024
- 2023MuseChat: A Conversational Music Recommendation System for Videos10 Oct 2023
- 2023Brain2Music: Reconstructing Music from Human Brain Activity20 Jul 2023