Abstract
Singer identification is the process of identifying or recognizing the singers based on the uniqueness in their singing voice. It is a challenging task in music information retrieval because of the combined instrumental music with the singing voice. The work presented in this paper recognizes a singer using Mel Frequency Cepstral Coefficient (MFCC) features and Chroma-Reduced Pitch (CRP) features with Support Vector Machines (SVM). The proposed technique for singer identification has two phases: feature extraction and identification. During the feature extraction phase, MFCC and CRP features are extracted from the songs in a database of popular music. In the second phase, the extracted features are trained with the SVM classifier. To evaluate our work, a dataset of 50 music clips was tested against the trained models of various singers. An equal error rate of 8% and 56% is achieved with SVM using MFCC and CRP features, respectively. By combining MFCC and CRP features at score level, an EER of 6.0% is obtained which indicates a significant increase in identification rate.
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Sangeetha, R., Nalini, N.J. (2020). Singer Identification Using MFCC and CRP Features with Support Vector Machines. In: Das, A., Nayak, J., Naik, B., Pati, S., Pelusi, D. (eds) Computational Intelligence in Pattern Recognition. Advances in Intelligent Systems and Computing, vol 999. Springer, Singapore. https://doi.org/10.1007/978-981-13-9042-5_25
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DOI: https://doi.org/10.1007/978-981-13-9042-5_25
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