Personal audio systems generate a local sound field for a listener while attenuating the sound energy at pre-defined quiet zones. In practice, system performance is sensitive to errors in the acoustic transfer functions between the sources and the zones. Regularization is commonly used to improve robustness, however, selecting a regularization parameter is not always straightforward. In this paper a design framework for robust reproduction is proposed, combining transfer function and error modeling. The framework allows a physical perspective on the regularization required for a system, based on the bound of assumed additive or multiplicative errors, which is obtained by acoustic modeling. Acoustic contrast control is separately combined with worst-case and probability-model optimization, exploiting limited knowledge of the potential error distribution. Monte-Carlo simulations show that these approaches give increased system robustness compared to the state of the art approaches for regularization parameter estimation, and experimental results verify that robust sound zone control is achieved in the presence of loudspeaker gain errors. Furthermore, by applying the proposed framework, in-situ transfer function measurements were reduced to a single measurement per loudspeaker, per zone, with limited acoustic contrast degradation of less than 2 dB over 100-3000 Hz compared to the fully measured regularized case.
Personal audio provides private and personalized listening experiences by generating sound zones in a shared space with minimal interference between zones. One challenge of the design is to achieve the best performance with a limited number of microphones and loudspeakers. In this paper, two modal domain methods for personal audio reproduction are compared. One is the spatial harmonic decomposition (SHD) based method and the other is the singular value decomposition (SVD) based method. It is demonstrated that the SVD based method provides a more efficient modal domain decomposition than the SHD method for 2.5D personal audio design. Simulation results show that the SVD based method outperforms the SHD one by up to 10 dB in terms of acoustic contrast and up to 17 dB in terms of reproduction error for a compact arc array with 5 loudspeakers, while requiring fewer microphones around the zone boundaries. The SVD based method retains the inherent efficiency of optimizing in a modal domain while avoiding the inherent geometric limitations of using SHD basis functions. Thus, this approach is advantageous for applications with flexible system geometries and a small number of loudspeakers and microphones.
Pressure matching (PM) and planarity control (PC) methods can be used to reproduce local sound with a certain orientation at the listening zone, while suppressing the sound energy at the quiet zone. In this letter, regularized PM and PC, incorporating coarse error estimation, are introduced to increase the robustness in non-ideal reproduction scenarios. Facilitated by this, the interaction between regularization, robustness, (tuned) personal audio optimization, and local directional performance is explored. Simulations show that under certain conditions, PC and weighted PM achieve comparable performance, while PC is more robust to a poorly selected regularization parameter.
Personal audio generates sound zones in a shared space to provide private and personalized listening experiences with minimized interference between consumers. Regularization has been commonly used to increase the robustness of such systems against potential perturbations in the sound reproduction. However, the performance is limited by the system geometry such as the number and location of the loudspeakers and controlled zones. This paper proposes a geometry optimization method to find the most geometrically robust approach for personal audio amongst all available candidate system placements. The proposed method aims to approach the most 'natural' sound reproduction so that the solo control of the listening zone coincidently accompanies the preferred quiet zone. Being formulated in the SVD-based modal domain, the method is demonstrated by applications in three typical personal audio optimizations, i.e., the acoustic contrast control, the pressure matching and the planarity control. Simulation results show that the proposed method can obtain the system geometry with better avoidance of 'occlusion', improved robustness to regularization, and improved broadband equalization.
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