Personal Sound Zones by Subband Filtering and Time Domain Optimization

• Published in: IEEE/ACM transactions on audio, speech, and language processing speech, and language processing, 2020, Vol.28, p.2684-2696
• Main Authors: Moles-Cases, Vicent, Pinero, Gema, de Diego, Maria, Gonzalez, Alberto
• Format: Article
• Language: English
• Subjects: Algorithms; Broadband; Computational complexity; Delays; Filter banks; Finite impulse response filters; Fourier transforms; Frequency domain analysis; Loudspeakers; Matching; Optimization; Partially stabilized zirconia; Personal audio; Sound filters; sound zones; subband filtering; Systems analysis; Time domain analysis; Versatility; Zirconium dioxide
• ISSN: 2329-9290; 2329-9304
• DOI: 10.1109/TASLP.2020.3023628

Personal Sound Zones (PSZ) systems aim to render independent sound signals to multiple listeners within a room by using arrays of loudspeakers. One of the algorithms used to provide PSZ is Weighted Pressure Matching (wPM), which computes the filters required to render a desired response in the listening zones while reducing the acoustic energy arriving to the quiet zones. This algorithm can be formulated in time and frequency domains. In general, the time-domain formulation (wPM-TD) can obtain good performance with shorter filters and delays than the frequency-domain formulation (wPM-FD). However, wPM-TD requires higher computation for obtaining the optimal filters. In this article, we propose a novel approach to the wPM algorithm named Weighted Pressure Matching with Subband Decomposition (wPM-SD), which formulates an independent time-domain optimization problem for each of the subbands of a Generalized Discrete Fourier Transform (GDFT) filter bank. Solving the optimization independently for each subband has two main advantages: 1) lower computational complexity than wPM-TD to compute the optimal filters; 2) higher versatility than the classic wPM algorithms, as it allows different configurations (sets of loudspeakers, filter lengths, etc.) in each subband. Moreover, filtering the input signals with a GDFT filter bank (as in wPM-SD) requires lower computational effort than broadband filtering (as in wPM-TD and wPM-FD), which is beneficial for practical PSZ systems. We present experimental evaluations showing that wPM-SD offers very similar performance to wPM-TD. In addition, two cases where the versatility of wPM-SD is beneficial for a PSZ system are presented and experimentally validated.