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

  • Noor Azilah Muda
  • Azah Kamilah Muda
  • Choo Yun Huoy
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 734)

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.

Keywords

Artificial immune system Modified AIS-based classifier Censoring and monitoring modules Classification Song genre 

Notes

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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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Noor Azilah Muda
    • 1
  • Azah Kamilah Muda
    • 1
  • Choo Yun Huoy
    • 1
  1. 1.Computational Intelligence and Technologies (CIT) Research Group, Center of Advanced Computing and Technologies, Faculty of Information and Communication TechnologyUniversiti Teknikal Malaysia MelakaDurian TunggalMalaysia

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