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This paper studies signal models for microphone array beamforming in the short-time-Fourier-transform (STFT) domain with long acoustic impulse responses. The major contributions are as follows. First, the signal modeling problem is investigated in the STFT domain and a general decomposition is proposed for the convolved source signal. Second, new insights into the array manifold are presented: the STFT of the windowed acoustic impulse response from the source to the sensors. Third, the structure of the reference signal is analyzed: it can be viewed as the output of a beamformer without considering the noise in the observation signal. Fourth, based on the new perspectives and decomposition, a signal model is derived based on the use of the superdirective beamformer. Finally, three performance measures are defined, based on which three optimal/suboptimal signal models are derived and their performance is assessed under different acoustic environments and analysis window lengths. The performance of the well-known minimum variance distortionless response (MVDR) beamformer is evaluated, which justifies the properties of the developed signal models. [ABSTRACT FROM AUTHOR]

Differential microphone arrays (DMAs), which are responsive to the differential acoustic pressure fields, have been used in a wide range of applications related to audio and speech. The core part of a DMA is the so-called differential beamformer, which is generally designed by placing a number of nulls in its beampattern to attenuate noise from some directions. But the presence of these nulls may cause some great issues, e.g., leading to suboptimal performance if the interference/noise is incident from directions other than the nulls’ directions, and making the beamformer less robust to sensors’ self noise and array imperfections. To overcome these problems, this letter is devoted to the design of differential beamformers with no nulls in its beampattern. A design method and its multistage implementation are presented and analyzed. An improved solution is then developed, which is able to form frequency-invariant beampatterns with no nulls in the frequency range of speech signals. Simulations are provided to illustrate the properties of the developed methods. [ABSTRACT FROM AUTHOR]

Differential beamformers with small-size microphone arrays are very attractive for audio and speech signal acquisition thanks to their high directivity and frequency-invariant spatial responses. However, such beamformers often suffer from significant white noise amplification at low frequencies, which makes their implementation in real-world systems challenging. One widely used way to circumvent this issue is to increase the number of microphones in the design of a given order differential beamformer, leading to the so-called robust differential beamformer in which the redundancy provided by the additional sensors are used to improve the white noise gain (WNG). But even with this robust solution, white noise amplification at low frequencies still exists. In this article, instead of trying to improve WNG, we adopt a method to render the white noise in such a way that it affects less the perception of the speech signal of interest. Specifically, we propose a binaural differential beamforming method in which a differential beamformer is designed with two sub-beamforming filters that simultaneously generates two outputs, one for the left ear and the other for the right ear. Motivated by psychoacoustic experiments, we design these two filters in such a way that they are orthogonal so that the coherence between the white noise at the binaural outputs is minimized while the coherence between diffuse noise is maximized. With the proposed binaural differential beamformers, the desired signal components and (amplified) white noise at the binaural differential beamformer's outputs are rendered into different directions or zones. Consequently, the human perception system can better distinguish the desired signal from white noise for improved speech intelligibility. The superiority of the proposed binaural beamforming technique is justified by simulations, experiments, as well as listening tests. [ABSTRACT FROM AUTHOR]

Differential beamforming combined with microphone arrays can be used in a wide range of applications related to acoustic and speech signal acquisition and recovery. A practical and useful method for designing differential beamformers is the so-called null-constrained method, which was developed based on linear arrays and requires only the nulls' information from the target directivity pattern. While it is effective and easy to use, this method is found not suitable for designing steerable differential beamformers with circular arrays. This paper reexamines this technique in the context of circular differential microphone arrays. By analyzing the properties of the circular array topology, the null-constrained method is extended to include symmetric constraints, which is inherent in the design of circular arrays. This extension yields a design method for fully steerable differential beamformers that require only minimum information from the target beampattern. Simulations justify the theoretical analysis and demonstrate the good properties of the developed method. [ABSTRACT FROM AUTHOR]

Microphone arrays are typically used in room acoustic environments to acquire high fidelity audio and speech signals while suppressing noise, interference, and reverberation. In many application scenarios, interference and reverberation may mainly come from a certain region, and it is therefore necessary to develop beamformers that can preserve signals of interest while minimizing the power of signals coming from the region where interference and reverberation dominate. For this purpose, this paper first reexamines the so-called front-to-back ratio and the classical supercardioid beamformer. To deal with the white noise amplification problem and the limited directivity factor associated with the supercardioid beamformer, a set of reduced-rank beamformers are deduced by using the well-known joint diagonalization technique, which can make compromises between the front-to-back ratio and the amount of white noise amplification or the directivity factor. Then, the definition of the front-to-back ratio is extended to a generalized version, from which another set of reduced-rank beamformers and their regularized versions are developed. Simulations are conducted to illustrate the properties and advantages of the proposed beamformers. [ABSTRACT FROM AUTHOR]

This letter deals with the problem of differential beamforming with microphone arrays of arbitrary planar geometry. By approximating the beampattern with the Jacobi-Anger expansion, it develops an algorithm that can form any specified frequency-invariant beampattern with a microphone array of any planar geometry as long as the sensors' coordinates are given and the spacing between neighboring sensors is smaller than the smallest wavelength. This method is rather general and it can be used to design differential beamformers with linear, circular, and concentric circular differential microphone arrays as well as differential arrays of arbitrary planar geometry where sensors are placed in any specified positions. [ABSTRACT FROM AUTHOR]

This paper develops an approach to beamforming with small spacing uniform linear microphone arrays based on the null-steering (NS) principle. It first formulates the beamforming problem from the conventional mean-squared error (MSE) criterion and its normalized version. Several NS algorithms are then derived for beamforming with the constraint of placing nulls to either a single direction or multiple angles. The difference and relationships between different algorithms are discussed and their performances are evaluated. These algorithms can be used to design either fixed or adaptive beamformers. In the former case, the resulting beamformers work as differential microphone arrays (DMAs) since they exhibit frequency-invariant beampatterns and their directivity factors are high with a given number of sensors. In the latter case, the resulting beamformers can be viewed as a combination of DMAs and single-channel noise reduction since they do not only exhibit frequency-invariant beampatterns but also can achieve noise reduction based on the noise statistics. [ABSTRACT FROM AUTHOR]

The performance of differential microphone arrays (DMAs) depends on many factors such as the number of sensors and the array geometry. This paper develops an approach that exploits nonuniform linear geometries and the minimum-norm filter to improve the robustness of DMAs against white noise. Unlike the conventional way that forms an N th-order DMA by subtractively combining the outputs of two DMAs of order N - 1 , this approach works in the short-time Fourier transform (STFT) domain and applies a complex weight to the output of each sensor and then sum the weighted outputs to form the beamforming output in every STFT subband. The minimum-norm filter is obtained by maximizing the white noise gain of the beamformer subject to the so-called fundamental constraints. The nonuniform linear arrays are created by adjusting the interelement spacing according to some rule. We show that the use of nonuniform linear geometries can significantly improve the robustness of DMAs, particularly at low frequencies. We also show that the diagonal loading technique can help improve the robustness of DMA beamformers, though the improvement is not significant. [ABSTRACT FROM AUTHOR]

The superdirective beamformer, while attractive for processing broadband acoustic signals, often suffers from the problem of white noise amplification. So, its application requires well-designed acoustic arrays with sensors of extremely low self-noise level, which is difficult if not impossible to attain. In this paper, a new binaural superdirective beamformer is proposed, which is divided into two sub-beamformers. Based on studies and facts in psychoacoustics, these two filters are designed in such a way that they are orthogonal to each other to make the white noise components in the binaural beamforming outputs incoherent while maximizing the output interaural coherence of the diffuse noise, which is important for the brain to localize the sound source of interest. As a result, the signal of interest in the binaural superdirective beamformer's outputs is in phase but the white noise components in the outputs are random phase, so the human auditory system can better separate the acoustic signal of interest from white noise by listening to the outputs of the proposed approach. Experimental results show that the derived binaural superdirective beamformer is superior to its conventional monaural counterpart. [ABSTRACT FROM AUTHOR]

• It develops a multichannel sparse linear prediction approach to time delay estimation of acoustics sources in reverberant and noisy environments, which can be viewed as a generalization of the previously developed multichannel cross-correlation coefficient (MCCC) algorithm and the multichannel spatio-temporal prediction (MCSTP) algorithm. • Based on the sparsity of the prediction coefficient matrix of speech signals, a class of algorithms are developed for TDE, including the multichannel spatio-temporal sparse prediction and the multichannel spatio-temporal group sparse prediction algorithms. • Through theoretical analysis and simulations, it is shown that the developed TDE algorithms are more robust to noise and reverberation than the MCCC and MCSTP algorithms. Time delay estimation (TDE), which is also called time difference of arrival estimation, is an important yet challenging problem in room acoustic environments where reverberation and noise coexist. The multichannel cross-correlation coefficient algorithm extends the traditional cross-correlation method from the two- to the multiple-channel cases and exploits the spatial information among all microphones to improve the robustness of TDE with respect to noise. The multichannel spatiotemporal prediction algorithm generalizes the multichannel cross-correlation coefficient algorithm by incorporating both the spatial and temporal information to make TDE robust to reverberation. This multichannel spatiotemporal prediction algorithm, however, is sensitive to noise. In this work, we attempt to improve the robustness of this algorithm by making it robust to both reverberation and noise. Based on the sparsity of the prediction coefficient matrix of speech signals, a class of multichannel sparse linear prediction algorithms, including the multichannel spatiotemporal sparse prediction and the multichannel spatiotemporal group sparse prediction, are developed for TDE. The multichannel cross-correlation coefficient and multichannel spatiotemporal prediction algorithms are unified from a TDE performance perspective via an F /ℓ 1 -norm (or F /ℓ 1,2 -norm) optimization model, which is solved by an alternating direction method of multipliers. The two new algorithms also respectively construct a set of time delay estimators, which make different tradeoffs between prewhitening and non-prewhitening by adjusting a regularization parameter. [ABSTRACT FROM AUTHOR]

Beamformers have been widely used to enhance signals from a desired direction and suppress noise and interfering signals from other directions. Constant beamwidth beamformers enable a fixed beamwidth over a wide range of frequencies. Most of the existing approaches to design constant beamwidth beamformers are based on optimization algorithms with high computational complexity and are often sensitive to microphone mismatches. Other existing methods are based on adjusting the number of sensors according to the frequency, which simplify the design, but cannot control the sidelobe level. Here, we propose a window-based technique to attain the beamwidth constancy, in which different shapes of standard window functions are applied for different frequency bins as the real weighting coefficients of microphones. Thereby, not only do we keep the beamwidth constant, but we also control the sidelobe level. Simulation results show the advantages of our method compared with existing methods, including lower sidelobe level, higher directivity factor, and higher white noise gain. [ABSTRACT FROM AUTHOR]

Due to their small size, differential microphone arrays (DMAs) are very attractive. Moreover, they have been effective in combating noise and reverberation. Recently, a new class of DMAs of different orders have been developed with the MacLaurin’s series and the frequency-independent patterns. However, the MacLaurin’s series does not approximate well the exponential function, which appears in the general definition of the beampattern, when the intersensor spacing is not small enough. To circumvent this problem, we propose in this paper to approximate the exponential function with the Jacobi–Anger expansion. Based on this approximation and the frequency-independent Chebyshev patterns, we derive first-, second-, and third-order DMAs. Furthermore, in order to improve the robustness of DMAs against white noise amplification, we propose to use more microphones combined with minimum-norm filters. It is also shown that the Jacobi–Anger expansion is optimal from a mean-squared error perspective. Simulations are carried out to evaluate the performance of the proposed DMAs. [ABSTRACT FROM AUTHOR]

After more than a century of accelerated advances in telecommunication technologies, people are no longer satisfied with talking to someone over a long distance and in real time. They want to collaborate through communication in a more produc-tive way with the feeling of being together and sharing the same environment, which we refer to as an immersive experience. This need offers great opportunities for multichannel acoustic and speech signal processing, and for new ideas of voice communication services infrastructure. In this article, we present a survey of the development of various immersive audio schemes in concert with this movement according to our involvement and insights. [ABSTRACT FROM PUBLISHER]

Abstract: Conventional multichannel noise reduction techniques are formulated by splitting the processed microphone observations into two terms: filtered noise-free speech and residual additive noise. The first term is treated as desired signal while the second is a nuisance. Then, the objective has typically been to reduce the nuisance while keeping the filtered speech as similar as possible to the clean speech. It turns out that this treatment of the overall filtered speech as the desired signal is inappropriate as will become clear soon. In this paper, we present a new study of the multichannel time-domain noise reduction filters. We decompose the noise-free microphone array observations into two components where the first is correlated with the target signal and perfectly coherent across the sensors while the second consists of residual interference. Then, well-known time-domain filters including the minimum variance distortionless response (MVDR), the space–time (ST) prediction, the maximum signal-to-noise ratio (SNR), the linearly constrained minimum variance (LCMV), the multichannel tradeoff, and Wiener filters are derived. Besides, the analytical performance evaluation of these time-domain filters is provided and new insights into their functioning are presented. Numerical results are finally given to corroborate our study. [Copyright &y& Elsevier]