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In this paper, we propose an architecture for music genre classification leveraging a Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN). The motivation behind this study is to recognize the importance of discerning essential spectral features for accurate genre classification in audio data. Focusing on the GTZAN dataset comprising ten music genres, our methodology involves intricate feature extraction, emphasizing Mel Frequency Cepstral Coefficients (MFCC). This transformation captures essential spectral features crucial for genre discernment. Beyond traditional methods, we incorporate Short-Term Fourier Transform (STFT) with advanced activations and signal processing techniques to enhance feature extraction. The CNN-LSTM model effectively captures spatial and temporal complexities in audio data, significantly to the domain of music genre classification. The outcomes underscore the performance of our proposed model, showcasing its potential for practical applications in music genre classifications, and compare the results with state-of-the-art methods.

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MGC: Music Genre Classification Using a Hybrid CNN-LSTM Model with MFCC Input

  • 会议论文

  • 发表日期：

    2026年

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  • Dattatreya N. Halyal,
  • Mahamadshiraj A. Bichhunavar,
  • Manjunath C. Pati,
  • Ramesh A. Tabib,
  • Basawaraj,
  • Uma Mudenagudi

摘要

In this paper, we propose an architecture for music genre classification leveraging a Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN). The motivation behind this study is to recognize the importance of discerning essential spectral features for accurate genre classification in audio data. Focusing on the GTZAN dataset comprising ten music genres, our methodology involves intricate feature extraction, emphasizing Mel Frequency Cepstral Coefficients (MFCC). This transformation captures essential spectral features crucial for genre discernment. Beyond traditional methods, we incorporate Short-Term Fourier Transform (STFT) with advanced activations and signal processing techniques to enhance feature extraction. The CNN-LSTM model effectively captures spatial and temporal complexities in audio data, significantly to the domain of music genre classification. The outcomes underscore the performance of our proposed model, showcasing its potential for practical applications in music genre classifications, and compare the results with state-of-the-art methods.