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Recognizing Music Features Pattern Using Modified Negative Selection Algorithm for Songs Genre Classification

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Hybrid Intelligent Systems (HIS 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 734))

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Abstract

Previous studies have proven that imitating the mechanism of recognizing alien cells is beneficial and provides so many solutions to the pattern recognition related problems. These efforts emulate the human immune system in recognizing the cells by considering every essential component or features of the subjects. In this research, the focus is on analyzing the music features patterns to recognize various songs genres by emphasizing the features from the artists’ voices, the melody of the music and even the sounds of the musical instruments used. Three fundamental music contents are investigated which are timbre, rhythm, and pitch based features. The main objective of this research is to recognize the music features from different genres using the modified negative selection algorithm fundamental procedures that are the censoring and monitoring modules. The results of the experimental works are remarkable and are comparable to previous works in the music recognition and classification works. In this highlight, stages of music recognition are emphasized where feature extraction, feature selection, and feature classification processes are explained. Comparison of performances between proposed algorithm and other classification technique are discussed.

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Acknowledgement

This work is funded by Universiti Teknikal Malaysia Melaka (UTeM) through the PJP High Impact Research Grant [PJP/2016/FTMK/HI3/S01473].

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Authors and Affiliations

  1. Computational Intelligence and Technologies (CIT) Research Group, Center of Advanced Computing and Technologies, Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100, Durian Tunggal, Melaka, Malaysia

    Noor Azilah Muda, Azah Kamilah Muda & Choo Yun Huoy

Authors
  1. Noor Azilah Muda
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  2. Azah Kamilah Muda
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  3. Choo Yun Huoy
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Corresponding author

Correspondence to Azah Kamilah Muda .

Editor information

Editors and Affiliations

  1. Machine Intelligence Research Labs, Auburn, WA, USA

    Ajith Abraham

  2. Department of Computer Science, South Asian University, Chanakyapuri, Delhi, India

    Pranab Kr. Muhuri

  3. Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia

    Azah Kamilah Muda

  4. Machine Intelligence Research Labs, Auburn, WA, USA

    Niketa Gandhi

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Muda, N.A., Muda, A.K., Huoy, C.Y. (2018). Recognizing Music Features Pattern Using Modified Negative Selection Algorithm for Songs Genre Classification. In: Abraham, A., Muhuri, P., Muda, A., Gandhi, N. (eds) Hybrid Intelligent Systems. HIS 2017. Advances in Intelligent Systems and Computing, vol 734. Springer, Cham. https://doi.org/10.1007/978-3-319-76351-4_25

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  • DOI: https://doi.org/10.1007/978-3-319-76351-4_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-76350-7

  • Online ISBN: 978-3-319-76351-4

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