Abstract
Singer identification is one of the challenging tasks in Music information retrieval (MIR) category. Music of India generates 4-5% of net revenue for a movie. Indian video songs include variety of singers. The research presented in this paper is to identify singer using MFCC and LPC coefficients from Indian video songs. Initially Audio portion is extracted from Indian video songs. Audio portion is divided into segments. For each segment, 13 Mel-frequency cepstral coefficients (MFCC) and 13 linear predictive coding (LPC) coefficients are computed. Principal component analysis method is used to reduce the dimensionality of segments. Singer models are trained using Naive bayes classifier and back propagation algorithm using neural network. The proposed approach is tested using different combinations of coefficients with male and female Indian singers.
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Ratanpara, T., Patel, N. (2015). Singer Identification Using MFCC and LPC Coefficients from Indian Video Songs. In: Satapathy, S., Govardhan, A., Raju, K., Mandal, J. (eds) Emerging ICT for Bridging the Future - Proceedings of the 49th Annual Convention of the Computer Society of India (CSI) Volume 1. Advances in Intelligent Systems and Computing, vol 337. Springer, Cham. https://doi.org/10.1007/978-3-319-13728-5_31
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DOI: https://doi.org/10.1007/978-3-319-13728-5_31
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13727-8
Online ISBN: 978-3-319-13728-5
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