Sound field recording and reproduction method using distributed spherical microphone arrays based on kernel ridge regression

The demand for sound field synthesis methods capable of reproducing wide areas is increasing. Ueno et al. (2018) proposed a technique that uses distributed microphones based on kernel ridge regression. However, it is difficult to synchronize all signals recorded by the microphones. In this study, we applied kernel ridge regression to signals captured by distributed spherical microphone arrays (SMAs) and proposed a sound field recording and reconstruction method that minimizes the effects of signal asynchronization and misalignment. In the proposed method, multiple SMAs are spatially arranged around the listening point to record the sound signals. The recorded signals are then transformed into spherical harmonic coefficients. Using these coefficients, secondary sound pressures at arbitrary positions are calculated. Finally, the sound field is reconstructed by applying kernel ridge regression to the calculated secondary sound pressures. The results of numerical simulations demonstrated that the proposed method reproduces an accurate sound field around the listening point. However, the findings also highlighted the importance of considering both the precision of the secondary sound pressures and the sufficiency of spatial information in the reconstruction region to achieve high reconstruction accuracy. These two factors involve a trade-off, especially in the high-frequency areas.