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Fisher Discriminative Embedding Low-Rank Sparse Representation for Music Genre Classification

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Abstract

This work focuses on a music genre classification method based on a sparse low-rank representation. Sparse low-rank representation is an effective method for learning classifiers, which aims to learn a row-sparse low-rank representation matrix to effectively ignore noise and identify subspace structures in data contaminated by outliers. However, these related methods fail to utilize the discriminative information to mine the rich supervision information available in the training samples. To address this issue, a novel Fisher Discriminative Embedding Low-Rank Sparse Representation (FDLRSR) classification algorithm is proposed based on the Fisher criterion, which results in stronger intra-class similarity and inter-class separability representation coefficients. Meanwhile, its two special cases, i.e., the Fisher Discriminative Embedding Low-Rank Representation (FDLR) and Fisher Discriminative Embedding Sparse Representation (FDSR) are also presented in this work. Specifically, the proposed classification method employs the FDLRSR algorithm coupled with the feature combinations consisting acoustic features and spectral features for music genre classification tasks by minimizing the residuals. Compared with the several state-of-the-art music genre classification methods, the proposed methods substantially improve the classification results on three widely used datasets, the GTZAN, ISMIR2004 and Homburg datasets, with the highest classification accuracies of 97.9% and 99.43%, which verify its effectiveness and availability.

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Data Availability

The data that support the findings of this study are openly available in the GTZAN Dataset - Music Genre Classification (http://marsyas.info/downloads/datasets.html), reference number [51], ISMIR2004 (https://ismir2004.ismir.net/genre_contest/index.html), reference number [6] and Homburg datasets (https://www-ai.cs.tu-dortmund.de/audio.html) reference number [21].

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Acknowledgements

Thank all the referees and the editorial board members for their insightful comments and suggestions, which improved our paper significantly. This study was funded by the National Natural Science Foundation of China under the Grants No.11501351.

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  1. Department of Mathematics, Shanghai University, Shanghai, 200444, P R China

    Xin Cai & Hongjuan Zhang

  2. Newtouch Center for Mathematics, Shanghai University, Shanghai, 200444, People’s Republic of China

    Hongjuan Zhang

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Cai, X., Zhang, H. Fisher Discriminative Embedding Low-Rank Sparse Representation for Music Genre Classification. Circuits Syst Signal Process (2024). https://doi.org/10.1007/s00034-024-02696-0

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