In this paper, we propose a low-latency beamforming method for target source extraction. Beamforming has been performed in the time-frequency domain and achieved promising results in offline applications. Meanwhile, it causes a long algorithmic delay due to the frame analysis. Such a delay is unacceptable in various low-latency real-time applications, including hearing aids. To reduce this delay, we propose a causal variant of the minimum power distortionless response (MPDR) beamformer. The proposed method constraints the non-causal components of the spatial filter to be zero in the optimization of the MPDR beamformer. The algorithmic delay is reduced to zero by applying the causal spatial filter in the time domain. We further propose to relax the distortionless constraint regarding the gain, which allows us to improve the extraction performance without a phase delay. The Douglas–Rachford splitting method and its online extension are adopted to solve the optimization problems of the proposed methods. In our experiment, the relaxed method outperformed various low-latency beamforming methods in terms of extraction performance.

In this paper, we propose a joint estimation method for the sampling rate offsets (SROs) of multiple recording devices. In wireless acoustic sensor networks, distributed microphones are connected to different analog-to-digital converters, and thus SROs occur on non-reference channels, which degrades the performance of various array signal processing techniques. To address this problem, we propose to jointly estimate and compensate SROs of all the non-reference channels. Since the proposed method is formulated as a multivariate non-convex optimization problem, we derive an efficient optimization algorithm on the basis of the majorization-minimization and majorizationequalization algorithms. We further propose to update SROs with only low-frequency components in the initial iterations to avoid undesired local optima. Our experimental results validate the effectiveness of the joint estimation of all SROs and demonstrate its advantage in subsequent array signal processing.

In this paper, we present a sound field interpolation for array signal processing (ASP) that is robust to rotation of a circular microphone array (CMA), and we evaluate beamforming as one of its applications. Most ASP methods assume a time-invariant acoustic transfer system (ATS) from sources to the microphone array. This assumption makes it challenging to perform ASP in real situations where sources and the microphone array can move. Therefore, considering a time-variant ATS is an essential task for the use of ASP. In this study, we focus on one such movement, the rotation of the CMA. Our method interpolates the sound field on the circumference of a circle, where microphones are equally spaced, based on the sampling theorem on the circle. The interpolation enables us to estimate the signals at the microphone positions before the rotation. Hence, conventional ASP, which assumes a time-invariant ATS, is applicable after interpolation without modification. We developed two beamforming schemes, one for batch and one for online processing, that combine the minimum power distortionless response beamformer and sound field interpolation. We evaluated the dependences of the interpolation on frequency and rotation angle using the signal-to-error ratio. Additionally, simulation results demonstrated that the two proposed schemes improve the beamformer's performance when the CMA rotates.

In this paper, we propose to simultaneously estimate all the sampling rate offsets (SROs) of multiple devices.In a distributed microphone array, the SRO is inevitable, which deteriorates the performance of array signal processing.Most of the existing SRO estimation methods focused on synchronizing two microphones.When synchronizing more than two microphones, we select one reference microphone and estimate the SRO of each non-reference microphone independently.Hence, the relationship among signals observed by non-reference microphones is not considered.To address this problem, the proposed method jointly optimizes all SROs based on a probabilistic model of a multichannel signal.The SROs and model parameters are alternately updated to increase the log-likelihood based on an auxiliary function.The effectiveness of the proposed method is validated on mixtures of various numbers of speakers.

In this paper, we present a novel framework of beamforming robust for a microphone array rotation. In most array signal processing methods, the time-invariant transfer system from a source to a microphone is assumed for calculating a spatial filter. This assumption makes it difficult to use the microphone array in real situations since sources and the microphone array may move. In this work, we focus on one such movement, the array’s rotation. The key in our method is to use a regularly circular microphone array where microphones are equally spaced on a circle’s circumference. Based on this, we propose a method to interpolate the sound field on the circumference. The interpolation enables us to estimate the signals at the microphone positions before the rotation even when the array rotates. Hence, the conventional array signal processing assuming the time-invariant system is applicable. Simulation results indicated that the proposed method improves the beamformer’s performance under the array rotation.

In this paper, we address signal enhancement in underdetermined situations and propose new beamforming algorithms. Beamforming in (over)determined situations can successfully reduce noise signals without distortion of a desired signal, which is known to be a desirable property, especially for automatic speech recognition systems. Even in underdetermined situations, time-frequency (TF) masking attains outstanding performance in noise reduction, although it tends to generate artifacts. Integrating these two approaches to benefit from both their advantages, we here propose time-frequency-bin-wise switching (TFS) and time-frequency-bin-wise linear combination (TFLC) beamforming. In the proposed methods, we utilize the best combination of beamformers among multiple beamformers at each TF bin, each of which suppresses a particular combination of interferers. First, we propose a general formulation of signal enhancement employing multiple spatial filters. Then a joint optimization problem of designing the spatial filters and estimating the suitable weights to combine them is considered under a unified minimum variance criterion. Finally, we present efficient algorithms to solve the problem. In experiments, we used an objective criterion that quantifies the amount of signal distortion caused by the enhancement function and confirmed that the proposed methods effectively suppress interferers without distortion of the target signal.

In this paper, we present a speech enhancement method using two microphones in underdetermined situations. Time-frequency (TF) binary masking is a conventional method of enhancing speech in underdetermined situations by appropriately multiplying each TF component by zero or one. Extending this method, we previously proposed a new method called the time-frequency-bin-wise switching (TFS) beamformer. In this method, we switch multiple preconstructed beamformers in each TF bin, each of which suppresses a particular interferer. However, this method requires the pre-estimation of beamformer filter coefficients using the target-active period and interferer-wise-active periods as the prior information. In this paper, to overcome this limitation, we formulate the switching and construction of spatial filters as a joint optimization problem, which can be understood from two viewpoints: the clustering of the most dominant interferer signal in each TF bin and the construction of a minimum variance distortionless response beamformer using such bins. In an experiment, we confirmed that the proposed method was superior to conventional TF masking and fixed beamforming during speech enhancement regardless of the direction of interferers.