  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%.
