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Music genre classification and recognition using convolutional neural network

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Abstract

The music genre classification system is crucial to users in the digital music business since it allows them to be more effective. Music suggestion and availability to consumers is one of the most successful uses of genre classification. Songs may be easily accessible by users when the genre of the song is recognized, and music recommendations to users are made simple with an accurate categorization system in place. Furthermore, automated genre categorization is necessary to tackle difficulties such as finding similar songs, identifying cultures that would enjoy certain music, and conducting surveys. Machine learning approaches have recently been shown to be useful in a variety of classification tasks, including music genre categorization. As a result, this research investigates the use of Convolutional Neural Networks (CNN) for music genre categorization. For this study, a fresh dataset of 1000 traditional music from ten genres was employed. Content-based features, were retrieved from the songs in the dataset and used as input into the classifier, as feature extraction is critical to audio analysis. We got the results of the accuracy level of the system is 98.9% with a precision of 98.7%, recall of 98.5%, and f1 score of 97.5%.

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Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

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Authors and Affiliations

  1. Shah and Anchor Kutchhi Engineering College, Chembur, Mumbai, India

    Nandkishor Narkhede

  2. Department of Electronics and Communication Engineering, Sir Padampat Singhania University, Udaipur, Rajasthan, India

    Sumit Mathur & Anand Bhaskar

  3. Department of Computer Science Engineering, Sir Padampat Singhania University, Udaipur, Rajasthan, India

    Mukesh Kalla

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  1. Nandkishor Narkhede
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  2. Sumit Mathur
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  3. Anand Bhaskar
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  4. Mukesh Kalla
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Correspondence to Nandkishor Narkhede.

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Narkhede, N., Mathur, S., Bhaskar, A. et al. Music genre classification and recognition using convolutional neural network. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-19243-3

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