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Recent development of optical interferometry enables us to measure sound without placing any device inside the sound field. In particular, parallel phase-shifting interferometry (PPSI) has realized advanced measurement of refractive index of air. Its novel application investigated very recently is simultaneous visualization of flow and sound, which had been difficult until PPSI enabled high-speed and accurate measurement several years ago. However, for understanding aerodynamic sound, separation of air flow and sound is necessary since they are mixed up in the observed video.


A sound field reconstruction method for a region including sources is proposed. Under the assumption of spatial sparsity of the sources,this reconstruction problem has been solved by using sparse decomposition algorithms with the discretization of the target region. Since this discretization leads to the off-grid problem, we previously proposed a gridless sound field decomposition method based on the reciprocity gap functional in the spherical harmonic domain.


The aim of spatial active noise control (ANC) is to attenuate noise over a certain space. Although a large-scale system is required to
achieve spatial ANC, mode-domain signal processing makes it possible to reduce the computational cost and improve the performance.
A higher-order source (HOS) has an advantage in sound field control due to its controllable directivity patterns. An array of HOS
can suppress an undesired exterior sound propagation while occupying a smaller physical space than a conventional omnidirectional


A gridless sound field decomposition method based on the reciprocity gap functional (RGF) is proposed. An intuitive and powerful way of reconstructing a sound field inside a region including sound sources is to decompose the sound field into Green's functions. Current methods based on sparse representation require discretization of the reconstruction region into grid points to construct the dictionary matrix; however, this procedure causes an off-grid problem and has a high computational cost.


In this paper, we propose a rate-distributed linearly constrained minimum variance (LCMV) beamformer for joint noise reduction and spatial cue preservation for assistive hearing in wireless acoustic sensor networks (WASNs). The WASN can consist of wireless communicating hearing aids, extended with additional wireless microphones. Due to the fact that each sensor node has a limited power budget, it is essential to consider the energy usage when designing algorithms for such WASNs.


We present pyroomacoustics, a software package aimed at the rapid development and testing of audio array processing algorithms.