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

• URL: http://arxiv.org/abs/2307.10773v1
• Date: Thu, 20 Jul 2023 11:10:06 GMT
• Title: Music Genre Classification with ResNet and Bi-GRU Using Visual Spectrograms
• Authors: Junfei Zhang
• Abstract summary: The limitations of manual genre classification have highlighted the need for a more advanced system. Traditional machine learning techniques have shown potential in genre classification, but fail to capture the full complexity of music data. 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)
• Score: 4.354842354272412
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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