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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.