Acoustic Beamforming Algorithms and Their Applications in Environmental Noise

Abstract Inferring optical response from other correlated optical response is highly demanded for vast applications such as biological imaging, material analysis, and optical characterization. This is distinguished from widely-studied forward and inverse designs, as it is boiled down to another different category, namely, spectra-to-spectra design. Whereas forward and inverse designs have been substantially explored across various physical scenarios, the spectra-to-spectra design remains elusive and challenging as it involves intractable many-to-many correspondences. Here, we first dabble in this uncharted area and propose a generation-elimination framework that can self-orient to the best output candidate. Such a framework has a strong built-in stochastically sampling capability that automatically generate diverse nominations and eliminate inferior nominations. As an example, we study terahertz metasurfaces to correlate the reflection spectra from low to high frequencies, where the inaccessible spectra are precisely forecasted without consulting structural information, reaching an accuracy of 98.77%. Moreover, an innovative dimensionality reduction approach is executed to visualize the distribution of the abstract correlated spectra data encoded in latent spaces. These results provide explicable perspectives for deep learning to parse complex physical processes, rather than “brute-force” black box, and facilitate versatile applications involving cross-wavelength information correlation. 

Correlating metasurface spectra with a generation-elimination framework

Multi-modal speech enhancement with bone-conducted speech in time domain

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.

DNN Based Multiframe Single-Channel Noise Reduction Filters

Previous studies demonstrate the impressive performance of residual neural networks (ResNet) in speaker verification. The ResNet models treat the time and frequency dimensions equally. They follow the default stride configuration designed for image recognition, where the horizontal and vertical axes exhibit similarities. This approach ignores the fact that time and frequency are asymmetric in speech representation. In this paper, we address this issue and look for optimal stride configurations specifically tailored for speaker verification. We represent the stride space on a trellis diagram, and conduct a systematic study on the impact of temporal and frequency resolutions on the performance and further identify two optimal points, namely Golden Gemini, which serves as a guiding principle for designing 2D ResNet-based speaker verification models. By following the principle, a state-of-the-art ResNet baseline model gains a significant performance improvement on VoxCeleb, SITW, and CNCeleb datasets with 7.70%/11.76% average EER/minDCF reductions, respectively, across different network depths (ResNet18, 34, 50, and 101), while reducing the number of parameters by 16.5% and FLOPs by 4.1%. We refer to it as Gemini ResNet. Further investigation reveals the efficacy of the proposed Golden Gemini operating points across various training conditions and architectures. Furthermore, we present a new benchmark, namely the Gemini DF-ResNet, using a cutting-edge model.

Golden Gemini is All You Need: Finding the Sweet Spots for Speaker Verification

This book explains the motivation for using microphone arrays as opposed to using a single sensor for sound acquisition. 

Microphone Arrays

On single-channel noise reduction with rank-deficient noise correlation matrix

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.

A Single-Input/Binaural-Output Antiphasic Speech Enhancement 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.

A Single-Channel Noise Reduction Filtering/Smoothing Technique in the Time Domain

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