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Beamforming has been used in a wide range of applications to extract the signal of interest from microphone array observations, which consist of not only the signal of interest, but also noise, interference, and reverberation. The recently proposed interference-controlled maximum noise reduction (ICMR) beamformer provides a flexible way to control the specified amount of the interference attenuation and noise suppression; but it requires accurate estimation of the manifold vector of the interference sources, which is challenging to achieve in real-world applications. To address this issue, we introduce an interference-controlled maximum noise reduction network (ICMRNet) in this study, which is a deep neural network (DNN)-based method for manifold vector estimation. With densely connected modified conformer blocks and the end-to-end training strategy, the interference manifold is learned directly from the observation signals. This approach, akin to ICMR, adeptly adapts to time-varying interference and demonstrates superior convergence rate and extraction efficacy as compared to the linearly constrained minimum variance (LCMV)-based neural beamformers when appropriate attenuation factors are selected. Moreover, via learning-based extraction, ICMRNet effectively suppresses reverberation components within the target signal. Comparative analysis against baseline methods validates the efficacy of the proposed method.

Distributed acoustic sensor network (DASN) refers to a sound acquisition system that consists of a collection of microphones randomly distributed across a wide acoustic area. Theory and methods for DASN are gaining increasing attention as the associated technologies can be used in a broad range of applications to solve challenging problems. However, unlike traditional microphone arrays or centralized systems, properly exploiting the redundancy among different channels in DASN is facing many challenges including but not limited to variations in pre-amplification gains, clocks, sensors' response, and signal-to-interference-plus-noise ratios (SINRs). Selecting appropriate sensors relevant to the task at hand is therefore crucial in DASN. In this work, we propose a speaker-dependent smoothed frame-level SINR estimation method for sensor selection in multi-speaker scenarios, specifically addressing source movement within DASN. Additionally, we devise an approach for similarity measurement to generate dynamic speaker embeddings resilient to variations in reference speech levels. Furthermore, we introduce a novel loss function that integrates classification and ordinal regression within a unified framework. Extensive simulations are performed and the results demonstrate the efficacy of the proposed method in accurately estimating smoothed frame-level SINR dynamically, yielding state-of-the-art performance.

Differential microphone arrays (DMAs), which enhance acoustic signals of interest by measuring both the acoustic pressure field and its spatial derivatives, find extensive use in various practical systems and acoustic products. A critical element of DMAs is the differential beamformer, traditionally designed to ensure that the designed beampattern closely matches the desired target directivity pattern. However, such beamformers may lack sufficient robustness in practice. To address the balance between robustness and beampattern accuracy, this letter proposes two types of beamformers: one prioritizes maximizing the white noise gain (WNG) while maintaining a specified mean-squared beampattern error (MSBE), and the other aims to minimize MSBE while adhering to a specified level of WNG. By transforming these design challenges into quadratic eigenvalue problems (QEPs), we derive explicit solutions for the proposed beamformers. Simulations are conducted to illustrate the performance characteristics of these beamformers.

Direction-of-arrival (DOA) estimation in environments with multiple sources and strong reverberation remains a great challenge. In this letter, we present a novel feature, the higher-order pseudo-intensity vector (HOPIV), derived from recordings obtained with a spherical microphone array. By exploiting the unique properties of the reactive intensity vector, which is derived from the HOPIV, we present a method to identify time-frequency points that are dominated by the direct path. We then propose a DOA estimation method that leverages the HOPIV's high spatial resolution for improving DOA estimation performance. Simulations and experiments show that the proposed method is able to yield superior performance compared to state-of-the-art techniques, even in highly reverberant environments.

Direction-of-arrival (DOA) estimation is challenging in complex acoustic environments with background noise and interference. Utilizing spherical microphone arrays, closed-form estimators can be derived, which are attractive for practical applications due to their computational efficiency, eliminating the need for exhaustive extremum searching. However, current closed-form estimators are susceptible to interference. To address this issue, we propose an estimator that directly computes the DOA of the desired source using the covariance matrix of the observation signals. This approach effectively mitigates the impact of interference when the covariance matrix is accurately estimated. Simulation results demonstrate the superior performance of the proposed method compared to the subspace pseudo-intensity vector (SSPIV) and relative harmonic coefficients (RHC) methods.

Linear differential microphone arrays (LDMAs) are commonly integrated into thin and portable devices to achieve high-fidelity speech acquisition. Traditional LDMAs typically consist of only omnidirectional microphones, which impose limitations on their ability to produce steerable spatial responses due to constraints in array element directivity and linear array geometry. A recent solution to this limitation involves integrating both omnidirectional and bidirectional microphones in LDMA design, enabling the creation of steerable spatial responses. This paper extends the core idea of integrating omnidirectional and bidirectional microphones, and develops a more general and comprehensive theory and method for designing steerable LDMAs. It makes two main contributions. Firstly, it introduces a general approach to designing steerable LDMAs, in which any type of directional microphones can be used. Secondly, it gives the minimum number of omnidirectional and directional microphones required to achieve a specific order of steerable LDMA. Simulations validate the proposed method and illustrate how omnidirectional and directional sensors can be combined to form the desired LDMAs.

Acoustic echo cancellation (AEC) is a crucial task in full duplex communications. As conventional linear filtering approaches are ineffective to deal with double-talk, various semi-blind source separation (SBSS)-based AEC algorithms are deceived, most of which are formulated and implemented in the frequency domain based on the multiplicative transfer function (MTF) model for computational efficiency. To avoid large latency and in order to deal with loudspeaker nonlinearities, the convolutive transfer function (CTF) model and odd power series expansion are leveraged, which are employed by numerous SBSS-based nonlinear AEC (SBSS-NAEC) algorithms. Conventional SBSS-NAEC methods estimate the series expansion coefficients and the CTF filter simultaneously making the number of free parameters to estimate large. Hence, the corresponding algorithms are computationally expensive and are difficult to optimize. In this work, we propose to decouple the series expansion coefficients and the CTF filters into a bilinear form and present a bilinear alternating optimization framework for estimating the model parameters. An alternating iterative projection (AIP) algorithm and an alternating element-wise iterative source steering (AEISS) algorithm are proposed. As the bilinear representation consists of less parameters compared to the conventional methods, the proposed algorithms not only improve the AEC performance but also reduce the computational complexity, which is validated by comprehensive simulations and experiments.