A Middle-Level Learning Feature Interaction Method with Deep Learning for Multi-Feature Music Genre Classification

Author:

Liu JinliangORCID,Wang Changhui,Zha Lijuan

Abstract

Nowadays, music genre classification is becoming an interesting area and attracting lots of research attention. Multi-feature model is acknowledged as a desirable technology to realize the classification. However, the major branches of multi-feature models used in most existed works are relatively independent and not interactive, which will result in insufficient learning features for music genre classification. In view of this, we exploit the impact of learning feature interaction among different branches and layers on the final classification results in a multi-feature model. Then, a middle-level learning feature interaction method based on deep learning is proposed correspondingly. Our experimental results show that the designed method can significantly improve the accuracy of music genre classification. The best classification accuracy on the GTZAN dataset can reach 93.65%, which is superior to most current methods.

Funder

Natural Science Foundation of Jiangsu Province

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering

Cited by 10 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A Hybrid Parallel Computing Architecture Based on CNN and Transformer for Music Genre Classification;Electronics;2024-08-21

2. A Short Survey and Comparison of CNN-Based Music Genre Classification Using Multiple Spectral Features;IEEE Access;2024

3. Lightweight Deep-Learning Based Music Genre Classification: A Study;2023 International Conference on System, Computation, Automation and Networking (ICSCAN);2023-11-17

4. Locally Activated Gated Neural Network for Automatic Music Genre Classification;Applied Sciences;2023-04-17

5. STI: Turbocharge NLP Inference at the Edge via Elastic Pipelining;Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2;2023-01-27

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