# Convolutional Neural Network Achieves Human-level Accuracy in Music Genre Classification

arXiv: https://arxiv.org/abs/1802.09697 · HTML: https://mlsanity.com/paper/1802.09697/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2018.

## Built on by (most cited first, 7 of 7)

- [AudRandAug: Random Image Augmentations for Audio Classification](https://mlsanity.com/paper/2309.04762/) (2023)
- [Identify ambiguous tasks combining crowdsourced labels by weighting Areas Under the Margin](https://mlsanity.com/paper/2209.15380/) (2022)
- [Proceedings of the 4th International Workshop on Reading Music Systems](https://mlsanity.com/paper/2211.13285/) (2022)
- [Music Genre Classification with ResNet and Bi-GRU Using Visual Spectrograms](https://mlsanity.com/paper/2307.10773/) (2023)
- [Formal Verification of Long Short-Term Memory based Audio Classifiers: A Star based Approach](https://mlsanity.com/paper/2311.12130/) (2023)
- [Transfer Learning in Vocal Education: Technical Evaluation of Limited Samples Describing Mezzo-soprano](https://mlsanity.com/paper/2410.23325/) (2024)
- [Attention-guided Spectrogram Sequence Modeling with CNNs for Music Genre Classification](https://mlsanity.com/paper/2411.14474/) (2024)

## Abstract (arXiv)

Music genre classification is one example of content-based analysis of music signals. Traditionally, human-engineered features were used to automatize this task and 61% accuracy has been achieved in the 10-genre classification. However, it's still below the 70% accuracy that humans could achieve in the same task. Here, we propose a new method that combines knowledge of human perception study in music genre classification and the neurophysiology of the auditory system. The method works by training a simple convolutional neural network (CNN) to classify a short segment of the music signal. Then, the genre of a music is determined by splitting it into short segments and then combining CNN's predictions from all short segments. After training, this method achieves human-level (70%) accuracy and the filters learned in the CNN resemble the spectrotemporal receptive field (STRF) in the auditory system.