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Environmental sound classification using optimum allocation sampling based empirical mode decomposition

Author

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  • Ahmad, Saad
  • Agrawal, Shubham
  • Joshi, Samta
  • Taran, Sachin
  • Bajaj, Varun
  • Demir, Fatih
  • Sengur, Abdulkadir

Abstract

Automatic environmental sound classification (ESC) is prominent in various fields like robotics, security, and crime investigation. In this paper, optimum allocation sampling (OAS)-based empirical mode method (EMD) is proposed for automatic ESC. The OAS provides the reduced homogeneous length sequence of each long length sound signal, which is further decomposed into band-limited intrinsic mode functions (IMFs) using EMD. The features namely approximate entropy (AE), permutation entropy (PE), log energy entropy (LE), interquartile range (IQR), and zero cross rate (ZCR) are extracted from the IMFs. The OAS-EMD based features used as input to multi-class least squares support vector machine (MC-LS-SVM) and extreme learning machine (ELM) classifiers for evaluation the performance of proposed method. Experimental results show an accuracy of 87.25% and 77.61% with MC-LS-SVM and ELM classifiers, respectively.

Suggested Citation

  • Ahmad, Saad & Agrawal, Shubham & Joshi, Samta & Taran, Sachin & Bajaj, Varun & Demir, Fatih & Sengur, Abdulkadir, 2020. "Environmental sound classification using optimum allocation sampling based empirical mode decomposition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    • Handle: RePEc:eee:phsmap:v:537:y:2020:i:c:s0378437119314955
    DOI: 10.1016/j.physa.2019.122613
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    Cited by:

    1. Ma, Changxi & Zhao, Mingxi & Huang, Xiaoting & Zhao, Yongpeng, 2024. "Optimized deep extreme learning machine for traffic prediction and autonomous vehicle lane change decision-making," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 633(C).

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