期刊
IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING
卷 31, 期 -, 页码 3331-3345出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TASLP.2023.3306712
关键词
Loudspeakers; Perturbation methods; Optimization; Speech processing; Robustness; Signal processing algorithms; Uncertainty; Biconvex optimization; complex Gaussian mixture model; expectation maximization; personal sound zone; robustness; semidefinite relaxation
Personal sound zone (PSZ) is a technique that uses an array of loudspeakers and digital signal processing tools to control spatial soundfield. This study presents a robust pressure matching (RPM) algorithm for sound zone generation, which models the acoustic transfer functions (ATFs) using a complex Gaussian mixture model (CGMM). The algorithm takes into account the effect of ATF perturbations and achieves better signal distortion energy and signal to interference ratio compared to traditional methods.
Personal sound zone (PSZ) refers to the technique that uses an array of loudspeakers and digital signal processing tools to achieve spatial soundfield control. To generate the target sound zones, this technique generally requires to know the acoustic transfer functions (ATFs) between the loudspeakers and the spots where soundfields are to be controlled. In practical applications, however, the true ATFs are never accessible and they have to be measured or estimated. Due to many sophisticated reasons, the measured ATFs generally deviate from the true ones, which may lead to significant degradation in performance of sound zone reproduction. In this work, a robust pressure matching (RPM) algorithm is presented for sound zone generation. It exploits a complex Gaussian mixture model (CGMM) to model the ATFs and their perturbations. The CGMM parameters are estimated using the expectation-maximization (EM) algorithm. To improve the robustness of the pressure matching method, an uncertainty constraint is applied to the ATF estimates and the pressure matching problem is then formulated as one of biconvex optimization. The coordinate descent algorithm is subsequently used to solve the optimization problem, thereby obtaining the optimal control filter. In comparison with the existing pressure matching methods without considering the effect of ATF perturbations, the presented algorithm is able to achieve lower normalized signal distortion energy and higher signal to interference ratio. Numerical simulations justify the effectiveness of the presented algorithm as well as its advantages over the traditional methods.
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