Boosting the Predictive Accurary of Singer Identification Using Discrete Wavelet Transform For Feature Extraction

• URL: http://arxiv.org/abs/2102.00550v1
• Date: Sun, 31 Jan 2021 21:58:55 GMT
• Title: Boosting the Predictive Accurary of Singer Identification Using Discrete Wavelet Transform For Feature Extraction
• Authors: Victoire Djimna Noyum, Younous Perieukeu Mofenjou, Cyrille Feudjio, Alkan G\"oktug and Ernest Fokou\'e
• Abstract summary: We study the performance of the Discrete Wavelet Transform (DWT) in comparison to the Mel Frequency Cepstral Coefficient (MFCC) We conclude that the best identification system consists of the DWT (db4) feature extraction introduced in this work combined with a linear support vector machine for identification resulting in a mean accuracy of 83.96%.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Facing the diversity and growth of the musical field nowadays, the search for precise songs becomes more and more complex. The identity of the singer facilitates this search. In this project, we focus on the problem of identifying the singer by using different methods for feature extraction. Particularly, we introduce the Discrete Wavelet Transform (DWT) for this purpose. To the best of our knowledge, DWT has never been used this way before in the context of singer identification. This process consists of three crucial parts. First, the vocal signal is separated from the background music by using the Robust Principal Component Analysis (RPCA). Second, features from the obtained vocal signal are extracted. Here, the goal is to study the performance of the Discrete Wavelet Transform (DWT) in comparison to the Mel Frequency Cepstral Coefficient (MFCC) which is the most used technique in audio signals. Finally, we proceed with the identification of the singer where two methods have experimented: the Support Vector Machine (SVM), and the Gaussian Mixture Model (GMM). We conclude that, for a dataset of 4 singers and 200 songs, the best identification system consists of the DWT (db4) feature extraction introduced in this work combined with a linear support vector machine for identification resulting in a mean accuracy of 83.96%.

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