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

TL;DR

This paper introduces a hybrid deep learning model combining ResNet and Bi-GRU to classify music genres from visual spectrograms, aiming to improve accuracy over traditional methods by capturing both spatial and temporal features.

Contribution

The study presents a novel hybrid ResNet and Bi-GRU model that leverages spectrograms for more effective music genre classification, addressing limitations of previous approaches.

Findings

Enhanced genre classification accuracy

Effective capture of spatial and temporal features

Potential improvements for music recommender systems

Abstract

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.