Music genre classification based on res-gated CNN and attention mechanism

Xie, Changjiang; Song, Huazhu; Zhu, Hao; Mi, Kaituo; Li, Zhouhan; Zhang, Yi; Cheng, Jiawen; Zhou, Haifeng; Li, Renjie; Cai, Huaiyu

doi:10.1007/s11042-023-15277-1

Cited by 6 publications

(1 citation statement)

References 36 publications

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“…At times, platforms permit non-professional users to contribute tags by making the tagging feature accessible, and these user-generated tagging data are incorporated into the music tags. While this approach reduces costs, it often results in numerous instances of mislabeling within categories [3]. Therefore, it is necessary to achieve music genre classification by computational methods.…”

Section: Introductionmentioning

confidence: 99%

Music Genre Classification Based on VMD-IWOA-XGBOOST

Gan,

Huang,

Shao

et al. 2024

Mathematics

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Music genre classification is significant to users and digital platforms. To enhance the classification accuracy, this study proposes a hybrid model based on VMD-IWOA-XGBOOST for music genre classification. First, the audio signals are transformed into numerical or symbolic data, and the crucial features are selected using the maximal information coefficient (MIC) method. Second, an improved whale optimization algorithm (IWOA) is proposed for parameter optimization. Third, the inner patterns of these selected features are extracted by IWOA-optimized variational mode decomposition (VMD). Lastly, all features are put into the IWOA-optimized extreme gradient boosting (XGBOOST) classifier. To verify the effectiveness of the proposed model, two open music datasets are used, i.e., GTZAN and Bangla. The experimental results illustrate that the proposed hybrid model achieves better performance than the other models in terms of five evaluation criteria.

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Section: Introductionmentioning

confidence: 99%

Music Genre Classification Based on VMD-IWOA-XGBOOST

Gan,

Huang,

Shao

et al. 2024

Mathematics

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A Short Survey and Comparison of CNN-Based Music Genre Classification Using Multiple Spectral Features

Seo,

Cho,

Teisseyre

et al. 2024

IEEE Access

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The goal of music genre classification is to identify the genre of given feature vectors representing certain characteristics of music clips. In addition, to improve the accuracy of music genre classification, considerable research has been conducted on extracting spectral features, which contain critical information for genre classification, from music clips and feeding these features into training models. In particular, recent studies argue that classification accuracy can be enhanced by employing multiple spectral features simultaneously. Consequently, fusing information from multiple spectral features is a critical consideration in designing music genre classification models. Hence, this paper provides a short survey of recent studies on music genre classification and compares the performance of the most recent CNN-based models with a newly devised model that employs a late fusion strategy for the multiple spectral features. Our empirical study of 12 public datasets, including Ballroom, ISMIR04, and GTZAN, showed that the late fusion CNN model outperforms other compared methods. Additionally, we performed an indepth analysis to validate the effectiveness of the late fusion strategy in music genre classification.

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Music Genre Classification Based on Functional Data Analysis

Shen,

Xiao

2024

IEEE Access

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Music genre classification based on res-gated CNN and attention mechanism

Cited by 6 publications

References 36 publications

Music Genre Classification Based on VMD-IWOA-XGBOOST

Music Genre Classification Based on VMD-IWOA-XGBOOST

A Short Survey and Comparison of CNN-Based Music Genre Classification Using Multiple Spectral Features

Music Genre Classification Based on Functional Data Analysis

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