Next Article in Journal
Snapshot Multi-Wavelength Birefringence Imaging
Previous Article in Journal
A Physical-Layer Security Cooperative Framework for Mitigating Interference and Eavesdropping Attacks in Internet of Things Environments
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Underdetermined Blind Source Separation of Audio Signals for Group Reared Pigs Based on Sparse Component Analysis

College of Information and Artificial Intelligence, Anhui Agricultural University, Hefei 230036, China
*
Author to whom correspondence should be addressed.
Sensors 2024, 24(16), 5173; https://doi.org/10.3390/s24165173
Submission received: 23 June 2024 / Revised: 3 August 2024 / Accepted: 9 August 2024 / Published: 10 August 2024
(This article belongs to the Section Intelligent Sensors)

Abstract

In order to solve the problem of difficult separation of audio signals collected in pig environments, this study proposes an underdetermined blind source separation (UBSS) method based on sparsification theory. The audio signals obtained by mixing the audio signals of pigs in different states with different coefficients are taken as observation signals, and the mixing matrix is first estimated from the observation signals using the improved AP clustering method based on the “two-step method” of sparse component analysis (SCA), and then the audio signals of pigs are reconstructed by L1-paradigm separation. Five different types of pig audio are selected for experiments to explore the effects of duration and mixing matrix on the blind source separation algorithm by controlling the audio duration and mixing matrix, respectively. With three source signals and two observed signals, the reconstructed signal metrics corresponding to different durations and different mixing matrices perform well. The similarity coefficient is above 0.8, the average recovered signal-to-noise ratio is above 8 dB, and the normalized mean square error is below 0.02. The experimental results show that different audio durations and different mixing matrices have certain effects on the UBSS algorithm, so the recording duration and the spatial location of the recording device need to be considered in practical applications. Compared with the classical UBSS algorithm, the proposed algorithm outperforms the classical blind source separation algorithm in estimating the mixing matrix and separating the mixed audio, which improves the reconstruction quality.
Keywords: pig audio; signal sparsification; AP clustering; l1 norm; blind source separation pig audio; signal sparsification; AP clustering; l1 norm; blind source separation

Share and Cite

MDPI and ACS Style

Pan, W.; Jiao, J.; Zhou, X.; Xu, Z.; Gu, L.; Zhu, C. Underdetermined Blind Source Separation of Audio Signals for Group Reared Pigs Based on Sparse Component Analysis. Sensors 2024, 24, 5173. https://doi.org/10.3390/s24165173

AMA Style

Pan W, Jiao J, Zhou X, Xu Z, Gu L, Zhu C. Underdetermined Blind Source Separation of Audio Signals for Group Reared Pigs Based on Sparse Component Analysis. Sensors. 2024; 24(16):5173. https://doi.org/10.3390/s24165173

Chicago/Turabian Style

Pan, Weihao, Jun Jiao, Xiaobo Zhou, Zhengrong Xu, Lichuan Gu, and Cheng Zhu. 2024. "Underdetermined Blind Source Separation of Audio Signals for Group Reared Pigs Based on Sparse Component Analysis" Sensors 24, no. 16: 5173. https://doi.org/10.3390/s24165173

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop