An efficient porcine acoustic signal denoising technique based on EEMD-ICA-WTD

Sunan Zhang, Jianyan Tian, Amit Banerjee, Jiangli Li

Research output: Contribution to journalArticle

Abstract

Automatic monitoring of group-housed pigs in real time through porcine acoustic signals has played a crucial role in automated farming. In the process of data collection and transmission, acoustic signals are generally interfered with noise. In this paper, an effective porcine acoustic signal denoising technique based on ensemble empirical mode decomposition (EEMD), independent component analysis (ICA), and wavelet threshold denoising (WTD) is proposed. Firstly, the porcine acoustic signal is decomposed into intrinsic mode functions (IMFs) by EEMD. In addition, permutation entropy (PE) is adopted to distinguish noise-dominant IMFs from the IMFs. Secondly, ICA is employed to extract the independent components (ICs) of the noise-dominant IMFs. The correlation coefficients of ICs and the first IMF are calculated to recognize noise ICs. The noise ICs will be removed. Then, WTD is applied to the other ICs. Finally, the porcine acoustic signal is reconstructed by the processed components. Experimental results show that the proposed method can effectively improve the denoising performance of porcine acoustic signal.

Original languageEnglish (US)
Article number2858740
JournalMathematical Problems in Engineering
Volume2019
DOIs
StatePublished - Jan 1 2019

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Signal denoising
Intrinsic Mode Function
Independent component analysis
Independent Component Analysis
Denoising
Acoustics
Wavelets
Ensemble
Decomposition
Decompose
Data Transmission
Acoustic noise
Correlation coefficient
Permutation
Entropy
Monitoring
Experimental Results

All Science Journal Classification (ASJC) codes

  • Mathematics(all)
  • Engineering(all)

Cite this

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abstract = "Automatic monitoring of group-housed pigs in real time through porcine acoustic signals has played a crucial role in automated farming. In the process of data collection and transmission, acoustic signals are generally interfered with noise. In this paper, an effective porcine acoustic signal denoising technique based on ensemble empirical mode decomposition (EEMD), independent component analysis (ICA), and wavelet threshold denoising (WTD) is proposed. Firstly, the porcine acoustic signal is decomposed into intrinsic mode functions (IMFs) by EEMD. In addition, permutation entropy (PE) is adopted to distinguish noise-dominant IMFs from the IMFs. Secondly, ICA is employed to extract the independent components (ICs) of the noise-dominant IMFs. The correlation coefficients of ICs and the first IMF are calculated to recognize noise ICs. The noise ICs will be removed. Then, WTD is applied to the other ICs. Finally, the porcine acoustic signal is reconstructed by the processed components. Experimental results show that the proposed method can effectively improve the denoising performance of porcine acoustic signal.",
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An efficient porcine acoustic signal denoising technique based on EEMD-ICA-WTD. / Zhang, Sunan; Tian, Jianyan; Banerjee, Amit; Li, Jiangli.

In: Mathematical Problems in Engineering, Vol. 2019, 2858740, 01.01.2019.

Research output: Contribution to journalArticle

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