TY - GEN
T1 - A NON-PARAMETRIC BAYESIAN MODEL FOR SUPPRESSING THE INTERFERENCE IN THE ACOUSTIC ARRAY MEASUREMENT
AU - Yu, Liang
AU - Zhang, Yongli
AU - Lyu, Mingsheng
AU - Wang, Ran
AU - Fang, Yong
AU - Jiang, Weikang
N1 - Publisher Copyright:
© 2023 Proceedings of the International Congress on Sound and Vibration. All rights reserved.
PY - 2023
Y1 - 2023
N2 - Microphone array technology has a wide range of applications in areas such as mechanical noise source identification and aircraft noise source identification. The acoustic array is not only applied in anechoic chamber measurements, but also in in-situ measurements, which is an essential requirement for current applications. However, the microphone array measurements are inevitably affected by background interference when we wish to implement in-situ measurements. This paper proposes a general array denoising algorithm to address this issue. Considering the complexity of background interference, a Gaussian mixture model that can fit any probability distribution is constructed. The background interference tends to have non-independently and non-identically distributed characteristics between different microphone channels. The hierarchical Dirichlet process is applied to the Gaussian mixture model to avoid selecting the Gaussian component number. At the same time, a low-rank model of the sound source signal is constructed according to its correlation characteristics between microphones. All involved parameters of the proposed model are solved by the variational Bayesian inference. The sound source signal and the complex background interference are eventually separated. The performance of the proposed algorithm is evaluated in numerical simulation and laboratory experiments. Both the effectiveness and robustness of the proposed algorithm in suppressing the complex background interference are also verified.
AB - Microphone array technology has a wide range of applications in areas such as mechanical noise source identification and aircraft noise source identification. The acoustic array is not only applied in anechoic chamber measurements, but also in in-situ measurements, which is an essential requirement for current applications. However, the microphone array measurements are inevitably affected by background interference when we wish to implement in-situ measurements. This paper proposes a general array denoising algorithm to address this issue. Considering the complexity of background interference, a Gaussian mixture model that can fit any probability distribution is constructed. The background interference tends to have non-independently and non-identically distributed characteristics between different microphone channels. The hierarchical Dirichlet process is applied to the Gaussian mixture model to avoid selecting the Gaussian component number. At the same time, a low-rank model of the sound source signal is constructed according to its correlation characteristics between microphones. All involved parameters of the proposed model are solved by the variational Bayesian inference. The sound source signal and the complex background interference are eventually separated. The performance of the proposed algorithm is evaluated in numerical simulation and laboratory experiments. Both the effectiveness and robustness of the proposed algorithm in suppressing the complex background interference are also verified.
KW - Acoustic array measurement
KW - Acoustic imaging with strong interference
KW - Noise measurement
KW - Nonparametric Bayesian model
KW - Variational Bayesian Inference
UR - http://www.scopus.com/inward/record.url?scp=85170639807&partnerID=8YFLogxK
M3 - 会议稿件
AN - SCOPUS:85170639807
T3 - Proceedings of the International Congress on Sound and Vibration
BT - Proceedings of the 29th International Congress on Sound and Vibration, ICSV 2023
A2 - Carletti, Eleonora
PB - Society of Acoustics
T2 - 29th International Congress on Sound and Vibration, ICSV 2023
Y2 - 9 July 2023 through 13 July 2023
ER -