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.

This paper studies the problem of target speaker signal exaction and antiphasic rendering with an array of microphones in the scenarios where there are two active speakers. Based on the important findings achieved in the psychoacoustic field as well as our recent works on single-channel speech enhancement, we present a rendering based approach in which a temporal convolutional network (TCN) is trained to take the multiple signals observed by the microphone array as its inputs and generate two output (binaural) signals. The TCN is trained in such a way that, when binaural output signals are listened by the listener with headsets, the speech signal from the desired speaker is perceived on one side of and close to the listener’s head, while the competing speech signal is perceived on the opposite side and also away from the listener’s head. Benefited from rendering and the signal-to-interference ratio (SIR) improvement, this antiphasic binaural presentation enables the listener to better focus on the target speaker’s signal while ignoring the impact of the competing speech. The modified rhyme tests (MRTs) are performed to validate the superiority of the proposed method.

While multiframe noise reduction filters, e.g., the multiframe Wiener and minimum variance distortionless response (MVDR) ones, have demonstrated great potential to improve both the subband and full-band signal-to-noise ratios (SNRs) by exploiting explicitly the interframe speech correlation, the implementation of such filters requires the knowledge of the interframe correlation coefficients for every subband, which are challenging to estimate in practice. In this work, we present a deep neural network (DNN) based method to estimate the interframe correlation coefficients and the estimated coefficients are subsequently fed into multiframe filters to achieve noise reduction. Unlike existing DNN based methods, which outputs the enhanced speech directly, the presented method combines deep learning and traditional methods, which gives more flexibility to optimize or tune noise reduction performance. Experimental results are presented to justify the properties of the presented methods.

Improving intelligibility of a speech signal of interest from its observations (with a single microphone) corrupted by additive noise has long been a challenging problem. Motivated by important findings achieved in the psychoacoustic field, we propose in this work a deep learning based method to render the noise and desired speech in the perceptual space such that the perception of the desired speech is least affected by the noise. Specifically, we adopt the temporal convolutional network (TCN) based structure to map the single-channel noisy observations into two binaural signals, one for the left ear and the other for the right ear. The TCN is trained in such a way that the desired speech and noise will be perceived to be in opposite directions when the listener listens to the binaural signals. This antiphasic binaural presentation enables the listener to better distinguish the desired speech from the annoying noise for improved speech intelligibility. The modified rhyme test is performed for evaluation and the results justify the superiority of the proposed method for speech intelligibility improvement.

In this paper, we present a single-channel smoothing-and-filtering technique for noise reduction in the time domain. Unlike traditional noise reduction methods, which directly apply a noise reduction filter to the noisy signal, the developed technique achieves noise reduction in two steps. It first applies a time smoothing window to the noisy signal, which, on the one hand, can help reduce high frequency noise and, on the other hand, can help leverage the correlation between successive signal samples. A noise reduction filter is then applied to the smoothed noisy signal to estimate the speech signal of interest. Three optimal and suboptimal noise reduction filters are derived, including the Wiener, maximum signal-to-noise-ratio (SNR), and tradeoff filters. Simulation results reveal that the developed method can produce better noise reduction performance, i.e., higher gains in the perceptual-evaluation-of-speech-quality (PESQ) score, than the traditional methods without smoothing.