TY - JOUR
T1 - Fast identification of coherent sound sources with the covariance matrix fitting method based on non-synchronous measurements
AU - Chen, Lin
AU - Xiao, Youhong
AU - Yu, Liang
AU - Yang, Tiejun
AU - Chen, Fangchao
AU - Zhang, Chenyu
AU - Ji, Huizhi
N1 - Publisher Copyright:
© 2024
PY - 2024/5/1
Y1 - 2024/5/1
N2 - The Non-Synchronous Measurements is particularly effective at expanding the operating frequency range of a single microphone array. The completed cross-spectral matrix of the synthetic array, when combined with high-resolution imaging algorithms, can yield accurate sound source identification results. However, when matrix completion algorithms are applied to large synthetic arrays, their computational complexity increases significantly. Furthermore, existing integrations with high-resolution imaging algorithms often encounter difficulties in the presence of coherent sound sources. To address this problem, this paper introduces the accelerated gradient descent algorithm for the covariance matrix fitting by orthogonal least squares. The matrix completion model for non-synchronous measurements is initially simplified through matrix decomposition, thereby enabling the rapid completion of the cross-spectral matrix using the accelerated gradient descent algorithm. This is then followed by the application of the covariance matrix fitting by orthogonal least squares to achieve quick and precise identification of coherent sound sources employing the completed cross-spectral matrix. The performance of the algorithm is evaluated using numerical simulations and validated through loudspeaker experiments in an anechoic chamber. The results from these simulations and experiments reveal that the proposed algorithm not only improves matrix completion performance on large synthetic arrays but also accurately identifies the locations and correlation coefficients of coherent sound sources.
AB - The Non-Synchronous Measurements is particularly effective at expanding the operating frequency range of a single microphone array. The completed cross-spectral matrix of the synthetic array, when combined with high-resolution imaging algorithms, can yield accurate sound source identification results. However, when matrix completion algorithms are applied to large synthetic arrays, their computational complexity increases significantly. Furthermore, existing integrations with high-resolution imaging algorithms often encounter difficulties in the presence of coherent sound sources. To address this problem, this paper introduces the accelerated gradient descent algorithm for the covariance matrix fitting by orthogonal least squares. The matrix completion model for non-synchronous measurements is initially simplified through matrix decomposition, thereby enabling the rapid completion of the cross-spectral matrix using the accelerated gradient descent algorithm. This is then followed by the application of the covariance matrix fitting by orthogonal least squares to achieve quick and precise identification of coherent sound sources employing the completed cross-spectral matrix. The performance of the algorithm is evaluated using numerical simulations and validated through loudspeaker experiments in an anechoic chamber. The results from these simulations and experiments reveal that the proposed algorithm not only improves matrix completion performance on large synthetic arrays but also accurately identifies the locations and correlation coefficients of coherent sound sources.
KW - Accelerated gradient algorithm
KW - Coherent sources
KW - Covariance matrix fitting
KW - Non-synchronous measurements
KW - Sound source identification
UR - http://www.scopus.com/inward/record.url?scp=85188786030&partnerID=8YFLogxK
U2 - 10.1016/j.ymssp.2024.111341
DO - 10.1016/j.ymssp.2024.111341
M3 - 文章
AN - SCOPUS:85188786030
SN - 0888-3270
VL - 213
JO - Mechanical Systems and Signal Processing
JF - Mechanical Systems and Signal Processing
M1 - 111341
ER -