TY - GEN
T1 - The Low-rank Gaussian Mixture Model with Interference Reference in the Acoustic Array Measurement for Background Interference Suppression
AU - Lyu, Mingsheng
AU - Yu, Liang
AU - Wang, Ran
AU - Fang, Yong
AU - Jiang, Weikang
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Background interference suppression for acoustic array measurements has essential applications in the aircraft industry, particularly during wind tunnel tests where interference from flow and various other measurement devices may affect measurement data. The low-rank Gaussian mixture model (LRGMM) has emerged as a potential method to suppress the strong and complex inference in the measurement. However, the performance and computational efficiency of the algorithm can be significantly affected by the number of Gaussian components in the model. This paper proposes a method for adaptively determining the number of Gaussian components in the Gaussian mixture model (GMM) using Bayesian information criteria (BIC) when interference reference has been measured. The model with fewer parameters is chosen by BIC, which improves computational efficiency while ensuring performance. The performance of the proposed method is validated by numerical simulation.
AB - Background interference suppression for acoustic array measurements has essential applications in the aircraft industry, particularly during wind tunnel tests where interference from flow and various other measurement devices may affect measurement data. The low-rank Gaussian mixture model (LRGMM) has emerged as a potential method to suppress the strong and complex inference in the measurement. However, the performance and computational efficiency of the algorithm can be significantly affected by the number of Gaussian components in the model. This paper proposes a method for adaptively determining the number of Gaussian components in the Gaussian mixture model (GMM) using Bayesian information criteria (BIC) when interference reference has been measured. The model with fewer parameters is chosen by BIC, which improves computational efficiency while ensuring performance. The performance of the proposed method is validated by numerical simulation.
KW - background interference suppression
KW - Bayesian information criteria
KW - Gaussian mixture model
KW - noise measurement
UR - http://www.scopus.com/inward/record.url?scp=85184806210&partnerID=8YFLogxK
U2 - 10.1109/ICICSP59554.2023.10388493
DO - 10.1109/ICICSP59554.2023.10388493
M3 - 会议稿件
AN - SCOPUS:85184806210
T3 - 2023 6th International Conference on Information Communication and Signal Processing, ICICSP 2023
SP - 932
EP - 936
BT - 2023 6th International Conference on Information Communication and Signal Processing, ICICSP 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th International Conference on Information Communication and Signal Processing, ICICSP 2023
Y2 - 23 September 2023 through 25 September 2023
ER -