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A fusion way of feature extraction for automatic categorization of music genres

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Abstract

Over the past decade, the invention of streaming services has led to the magnification of the music industry. With a plethora of available song choices, there is a dire need for recommendation techniques to help listeners discover music genres complementing their palate. This makes a vital need for automatic music genre categorization systems. With this objective, in this work fusion of direct and indirect features is introduced for the automatic categorization of music genres. In direct Feature Extraction (FE), the physical characteristics of music genres are assessed by timbral, chroma, and source separation-based features. In indirect FE, tunable Q-Wavelet transform and Teager energy operator are used to explore the non-linear characteristics of music signals. The proposed algorithm is examined on the GTZAN dataset, primarily focusing on the four-class classification problem. The introduced features are tested with multiple machine learning techniques to explore the best for music genre categorization. The wide neural network classifier with a single fully connected layer churned out optimal performance fetching an overall accuracy and F1 score of 95.8% and 95.82%, respectively. The proposed algorithm also outperforms most of the state-of-the-art techniques for the given dataset.

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Data availability

The dataset that support the findings of this study is belongs to [GTZAN] and it is available on Kaggle.

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Authors and Affiliations

  1. Delhi Technological University (DTU), Delhi, Shahbad Daulatpur, New Delhi, 110042, India

    Dhruv Sharma, Sachin Taran & Anukul Pandey

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  1. Dhruv Sharma
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  2. Sachin Taran
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Correspondence to Sachin Taran.

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Sharma, D., Taran, S. & Pandey, A. A fusion way of feature extraction for automatic categorization of music genres. Multimed Tools Appl 82, 25015–25038 (2023). https://doi.org/10.1007/s11042-023-14371-8

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