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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.


Active noise control (ANC) over a sizeable space requires a large number of reference and error microphones to satisfy the spatial Nyquist sampling criterion, which limits the feasibility of practical realization of such systems. This paper proposes a mode-domain feedforward ANC method to attenuate the noise field over a large space while reducing the number of microphones required.


We present a source separation system for high-order ambisonics (HOA) contents. We derive a multichannel spatial filter from a mask estimated by a long short-term memory (LSTM) recurrent neural network. We combine one channel of the mixture with the outputs of basic HOA beamformers as inputs to the LSTM, assuming that we know the directions of arrival of the directional sources. In our experiments, the speech of interest can be corrupted either by diffuse noise or by an equally loud competing speaker.