Multizone sound field reproduction with direction-of-arrival-distribution-based regularization and its application to binaural-centered mode-matching

In higher-order Ambisonics, a framework for sound field reproduction, secondary-source driving signals are generally obtained by regularized mode matching. The authors have proposed a regularization technique based on direction-of-arrival (DoA) distribution of wavefronts in the primary sound field. Such DoA-distribution-based regularization enables a suppression of excessively large driving signal gains for secondary sources that are in the directions far from the primary source direction. This improves the reproduction accuracy at regions away from the reproduction center. First, this study applies the DoA-distribution-based regularization to a multizone sound field reproduction based on the addition theorem. Furthermore, the regularized multizone sound field reproduction is extended to a binaural-centered mode matching (BCMM), which produces two reproduction points, one at each ear, to avoid a degraded reproduction accuracy due to a shrinking sweet spot at higher frequencies. Free-field and binaural simulations were numerically performed to examine the effectiveness of the DoA-distribution-based regularization on the multizone sound field reproduction and the BCMM.

Sound field decomposition based on two-stage neural networks

A method for sound field decomposition based on neural networks is proposed. The method comprises two stages: a sound field separation stage and a single-source localization stage. In the first stage, the sound pressure at microphones synthesized by multiple sources is separated into one excited by each sound source. In the second stage, the source location is obtained as a regression from the sound pressure at microphones consisting of a single sound source. The estimated location is not affected by discretization because the second stage is designed as a regression rather than a classification. Datasets are generated by simulation using Green's function, and the neural network is trained for each frequency. Numerical experiments reveal that, compared with conventional methods, the proposed method can achieve higher source-localization accuracy and higher sound-field-reconstruction accuracy.