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Singer Identification Based on Artificial Neural Network

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Advanced Informatics for Computing Research (ICAICR 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1075))

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Abstract

Music is a vocal or instrumental sounds (or combined) presented in such a way as to create, harmony, and expression of emotion. Present days accurately singer identification is necessary for music indexing and retrieval purpose. This paper proposes a unique features extraction algorithm for singer identification. In this paper, seven singers with five vocal songs are considered for singer identification. The most potential Mel-Frequency-Cepstral Coefficient (MFCC) based feature extraction algorithm, and an artificial neural network (ANN) classifier has been applied for the singer identification purpose. Four multilayer neural network training algorithm such as Levenberg-Marquardt, Bayesian regularization Backpropagation, Scaled Conjugate Gradient, and One-step secant Backpropagation algorithm has been used to classify the seven different singers voices. Three different feature extraction technique, such as MFCC, MFCC with five different musical features and MFCC with ten different musical features has been considered for the feature extraction. The highest training and testing accuracy have been achieved through this algorithm is 98.3% and 88.6%. Classification accuracy varies with musical features and classification algorithms.

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Acknowledgments

The authors would like to acknowledge the University Grant Commission (Ministry of Human Resource Development) for financial support in carrying out the research work.

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Authors and Affiliations

  1. ECE Department, BIT-Mesra, Ranchi, Jharkhand, India

    Sharmila Biswas & Sandeep Singh Solanki

Authors
  1. Sharmila Biswas
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  2. Sandeep Singh Solanki
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Corresponding author

Correspondence to Sharmila Biswas .

Editor information

Editors and Affiliations

  1. Papua New Guinea University of Technology, Lae, Papua New Guinea

    Ashish Kumar Luhach

  2. Computer Science Department, Namibia University of Science and Technology, Windhoek, Namibia

    Dharm Singh Jat

  3. Universiti Malaysia Pahang, Pekan, Pahang, Malaysia

    Kamarul Bin Ghazali Hawari

  4. School of Computing, University of Eastern Finland, Kuopio, Finland

    Xiao-Zhi Gao

  5. Department of Mathematics and Computing Science, Saint Mary's University, Halifax, Canada

    Pawan Lingras

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Biswas, S., Solanki, S.S. (2019). Singer Identification Based on Artificial Neural Network. In: Luhach, A., Jat, D., Hawari, K., Gao, XZ., Lingras, P. (eds) Advanced Informatics for Computing Research. ICAICR 2019. Communications in Computer and Information Science, vol 1075. Springer, Singapore. https://doi.org/10.1007/978-981-15-0108-1_36

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  • DOI: https://doi.org/10.1007/978-981-15-0108-1_36

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  • Print ISBN: 978-981-15-0107-4

  • Online ISBN: 978-981-15-0108-1

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