# Predicting Artificial Neural Network Representations to Learn Recognition Model for Music Identification from Brain Recordings

arXiv: https://arxiv.org/abs/2412.15560 · HTML: https://mlsanity.com/paper/2412.15560/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 49 references that are in the map.
- First posted on arXiv 20 Dec 2024.

## Builds on (most cited first, 15 of 15)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Contrastive Learning of Medical Visual Representations from Paired Images and Text](https://mlsanity.com/paper/2010.00747/) (2020)
- [Decoding speech perception from non-invasive brain recordings](https://mlsanity.com/paper/2208.12266/) (2022)
- [Toward a realistic model of speech processing in the brain with self-supervised learning](https://mlsanity.com/paper/2206.01685/) (2022)
- [Self-supervised models of audio effectively explain human cortical responses to speech](https://mlsanity.com/paper/2205.14252/) (2022)
- [Brain2Music: Reconstructing Music from Human Brain Activity](https://mlsanity.com/paper/2307.11078/) (2023)
- [Naturalistic Music Decoding from EEG Data via Latent Diffusion Models](https://mlsanity.com/paper/2405.09062/) (2024)
- [Deep Feature Learning for EEG Recordings](https://mlsanity.com/paper/1511.04306/) (2015)
- [GuessTheMusic: Song Identification from Electroencephalography response](https://mlsanity.com/paper/2009.08793/) (2020)
- [Enhancing Affective Representations of Music-Induced EEG through Multimodal Supervision and latent Domain Adaptation](https://mlsanity.com/paper/2202.09750/) (2022)
- [EEG2Mel: Reconstructing Sound from Brain Responses to Music](https://mlsanity.com/paper/2207.13845/) (2022)
- [Deep Learning of Human Perception in Audio Event Classification](https://mlsanity.com/paper/1809.00502/) (2018)
- [Image-based eeg classification of brain responses to song recordings](https://mlsanity.com/paper/2202.03265/) (2022)
- [Classifying Songs with EEG](https://mlsanity.com/paper/2010.04087/) (2020)

## Built on by (most cited first, 3 of 3)

- [SSDLabeler: Realistic semi-synthetic data generation for multi-label artifact classification in EEG](https://mlsanity.com/paper/2512.05500/) (2025)
- [Decoding Selective Auditory Attention to Musical Elements in Ecologically Valid Music Listening](https://mlsanity.com/paper/2512.05528/) (2025)
- [Expectation and Acoustic Neural Network Representations Enhance Music Identification from Brain Activity](https://mlsanity.com/paper/2603.03190/) (2026)

## Abstract (arXiv)

Recent studies have demonstrated that the representations of artificial neural networks (ANNs) can exhibit notable similarities to cortical representations when subjected to identical auditory sensory inputs. In these studies, the ability to predict cortical representations is probed by regressing from ANN representations to cortical representations. Building upon this concept, our approach reverses the direction of prediction: we utilize ANN representations as a supervisory signal to train recognition models using noisy brain recordings obtained through non-invasive measurements. Specifically, we focus on constructing a recognition model for music identification, where electroencephalography (EEG) brain recordings collected during music listening serve as input. By training an EEG recognition model to predict ANN representations-representations associated with music identification-we observed a substantial improvement in classification accuracy. This study introduces a novel approach to developing recognition models for brain recordings in response to external auditory stimuli. It holds promise for advancing brain-computer interfaces (BCI), neural decoding techniques, and our understanding of music cognition. Furthermore, it provides new insights into the relationship between auditory brain activity and ANN representations.