# Music Genre Classification with ResNet and Bi-GRU Using Visual Spectrograms

arXiv: https://arxiv.org/abs/2307.10773 · HTML: https://mlsanity.com/paper/2307.10773/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 46 references that are in the map.
- First posted on arXiv 20 Jul 2023.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [Speech Recognition with Deep Recurrent Neural Networks](https://mlsanity.com/paper/1303.5778/) (2013)
- [A Critical Review of Recurrent Neural Networks for Sequence Learning](https://mlsanity.com/paper/1506.00019/) (2015)
- [Bottom-up Broadcast Neural Network For Music Genre Classification](https://mlsanity.com/paper/1901.08928/) (2019)
- [Convolutional Neural Network Achieves Human-level Accuracy in Music Genre Classification](https://mlsanity.com/paper/1802.09697/) (2018)
- [Music Genre Classification using Machine Learning Techniques](https://mlsanity.com/paper/1804.01149/) (2018)
- [Comparing Time and Frequency Domain for Audio Event Recognition Using Deep Learning](https://mlsanity.com/paper/1603.05824/) (2016)
- [Classifying Variable-Length Audio Files with All-Convolutional Networks and Masked Global Pooling](https://mlsanity.com/paper/1607.02857/) (2016)
- [Acoustic Scene Classification with Spectrogram Processing Strategies](https://mlsanity.com/paper/2007.03781/) (2020)

## Abstract (arXiv)

Music recommendation systems have emerged as a vital component to enhance user experience and satisfaction for the music streaming services, which dominates music consumption. The key challenge in improving these recommender systems lies in comprehending the complexity of music data, specifically for the underpinning music genre classification. The limitations of manual genre classification have highlighted the need for a more advanced system, namely the Automatic Music Genre Classification (AMGC) system. While traditional machine learning techniques have shown potential in genre classification, they heavily rely on manually engineered features and feature selection, failing to capture the full complexity of music data. On the other hand, deep learning classification architectures like the traditional Convolutional Neural Networks (CNN) are effective in capturing the spatial hierarchies but struggle to capture the temporal dynamics inherent in music data. To address these challenges, this study proposes a novel approach using visual spectrograms as input, and propose a hybrid model that combines the strength of the Residual neural Network (ResNet) and the Gated Recurrent Unit (GRU). This model is designed to provide a more comprehensive analysis of music data, offering the potential to improve the music recommender systems through achieving a more comprehensive analysis of music data and hence potentially more accurate genre classification.