Parallel Gated Neural Network With Attention Mechanism For Speech Enhancement

Deep learning algorithm are increasingly used for speech enhancement (SE). In supervised methods, global and local information is required for accurate spectral mapping. A key restriction is often poor capture of key contextual information. To leverage long-term for target speakers and compensate distortions of cleaned speech, this paper adopts a sequence-to-sequence (S2S) mapping structure and proposes a novel monaural speech enhancement system, consisting of a Feature Extraction Block (FEB), a Compensation Enhancement Block (ComEB) and a Mask Block (MB). In the FEB a U-net block is used to extract abstract features using complex-valued spectra with one path to suppress the background noise in the magnitude domain using masking methods and the MB takes magnitude features from the FEBand compensates the lost complex-domain features produced from ComEB to restore the final cleaned speech. Experiments are conducted on the Librispeech dataset and results show that the proposed model obtains better performance than recent models in terms of ESTOI and PESQ scores.Comment: 5 pages, 6 figures, references adde

Explicit Estimation of Magnitude and Phase Spectra in Parallel for High-Quality Speech Enhancement

Phase information has a significant impact on speech perceptual quality and intelligibility. However, existing speech enhancement methods encounter limitations in explicit phase estimation due to the non-structural nature and wrapping characteristics of the phase, leading to a bottleneck in enhanced speech quality. To overcome the above issue, in this paper, we proposed MP-SENet, a novel Speech Enhancement Network which explicitly enhances Magnitude and Phase spectra in parallel. The proposed MP-SENet adopts a codec architecture in which the encoder and decoder are bridged by time-frequency Transformers along both time and frequency dimensions. The encoder aims to encode time-frequency representations derived from the input distorted magnitude and phase spectra. The decoder comprises dual-stream magnitude and phase decoders, directly enhancing magnitude and wrapped phase spectra by incorporating a magnitude estimation architecture and a phase parallel estimation architecture, respectively. To train the MP-SENet model effectively, we define multi-level loss functions, including mean square error and perceptual metric loss of magnitude spectra, anti-wrapping loss of phase spectra, as well as mean square error and consistency loss of short-time complex spectra. Experimental results demonstrate that our proposed MP-SENet excels in high-quality speech enhancement across multiple tasks, including speech denoising, dereverberation, and bandwidth extension. Compared to existing phase-aware speech enhancement methods, it successfully avoids the bidirectional compensation effect between the magnitude and phase, leading to a better harmonic restoration. Notably, for the speech denoising task, the MP-SENet yields a state-of-the-art performance with a PESQ of 3.60 on the public VoiceBank+DEMAND dataset.Comment: Submmited to IEEE Transactions on Audio, Speech and Language Processin

A General Unfolding Speech Enhancement Method Motivated by Taylor's Theorem

While deep neural networks have facilitated significant advancements in the field of speech enhancement, most existing methods are developed following either empirical or relatively blind criteria, lacking adequate guidelines in pipeline design. Inspired by Taylor's theorem, we propose a general unfolding framework for both single- and multi-channel speech enhancement tasks. Concretely, we formulate the complex spectrum recovery into the spectral magnitude mapping in the neighborhood space of the noisy mixture, in which an unknown sparse term is introduced and applied for phase modification in advance. Based on that, the mapping function is decomposed into the superimposition of the 0th-order and high-order polynomials in Taylor's series, where the former coarsely removes the interference in the magnitude domain and the latter progressively complements the remaining spectral detail in the complex spectrum domain. In addition, we study the relation between adjacent order terms and reveal that each high-order term can be recursively estimated with its lower-order term, and each high-order term is then proposed to evaluate using a surrogate function with trainable weights so that the whole system can be trained in an end-to-end manner. Given that the proposed framework is devised based on Taylor's theorem, it possesses improved internal flexibility. Extensive experiments are conducted on WSJ0-SI84, DNS-Challenge, Voicebank+Demand, spatialized Librispeech, and L3DAS22 multi-channel speech enhancement challenge datasets. Quantitative results show that the proposed approach yields competitive performance over existing top-performing approaches in terms of multiple objective metrics.Comment: Submitted to TASLP, revised version, 17 page

CMGAN: Conformer-Based Metric-GAN for Monaural Speech Enhancement

Convolution-augmented transformers (Conformers) are recently proposed in various speech-domain applications, such as automatic speech recognition (ASR) and speech separation, as they can capture both local and global dependencies. In this paper, we propose a conformer-based metric generative adversarial network (CMGAN) for speech enhancement (SE) in the time-frequency (TF) domain. The generator encodes the magnitude and complex spectrogram information using two-stage conformer blocks to model both time and frequency dependencies. The decoder then decouples the estimation into a magnitude mask decoder branch to filter out unwanted distortions and a complex refinement branch to further improve the magnitude estimation and implicitly enhance the phase information. Additionally, we include a metric discriminator to alleviate metric mismatch by optimizing the generator with respect to a corresponding evaluation score. Objective and subjective evaluations illustrate that CMGAN is able to show superior performance compared to state-of-the-art methods in three speech enhancement tasks (denoising, dereverberation and super-resolution). For instance, quantitative denoising analysis on Voice Bank+DEMAND dataset indicates that CMGAN outperforms various previous models with a margin, i.e., PESQ of 3.41 and SSNR of 11.10 dB.Comment: 16 pages, 10 figures and 5 tables. arXiv admin note: text overlap with arXiv:2203.1514

Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments

Eliminating the negative effect of non-stationary environmental noise is a long-standing research topic for automatic speech recognition that stills remains an important challenge. Data-driven supervised approaches, including ones based on deep neural networks, have recently emerged as potential alternatives to traditional unsupervised approaches and with sufficient training, can alleviate the shortcomings of the unsupervised methods in various real-life acoustic environments. In this light, we review recently developed, representative deep learning approaches for tackling non-stationary additive and convolutional degradation of speech with the aim of providing guidelines for those involved in the development of environmentally robust speech recognition systems. We separately discuss single- and multi-channel techniques developed for the front-end and back-end of speech recognition systems, as well as joint front-end and back-end training frameworks

TF-GridNet: Integrating Full- and Sub-Band Modeling for Speech Separation

We propose TF-GridNet for speech separation. The model is a novel multi-path deep neural network (DNN) integrating full- and sub-band modeling in the time-frequency (T-F) domain. It stacks several multi-path blocks, each consisting of an intra-frame full-band module, a sub-band temporal module, and a cross-frame self-attention module. It is trained to perform complex spectral mapping, where the real and imaginary (RI) components of input signals are stacked as features to predict target RI components. We first evaluate it on monaural anechoic speaker separation. Without using data augmentation and dynamic mixing, it obtains a state-of-the-art 23.5 dB improvement in scale-invariant signal-to-distortion ratio (SI-SDR) on WSJ0-2mix, a standard dataset for two-speaker separation. To show its robustness to noise and reverberation, we evaluate it on monaural reverberant speaker separation using the SMS-WSJ dataset and on noisy-reverberant speaker separation using WHAMR!, and obtain state-of-the-art performance on both datasets. We then extend TF-GridNet to multi-microphone conditions through multi-microphone complex spectral mapping, and integrate it into a two-DNN system with a beamformer in between (named as MISO-BF-MISO in earlier studies), where the beamformer proposed in this paper is a novel multi-frame Wiener filter computed based on the outputs of the first DNN. State-of-the-art performance is obtained on the multi-channel tasks of SMS-WSJ and WHAMR!. Besides speaker separation, we apply the proposed algorithms to speech dereverberation and noisy-reverberant speech enhancement. State-of-the-art performance is obtained on a dereverberation dataset and on the dataset of the recent L3DAS22 multi-channel speech enhancement challenge.Comment: In submission. A sound demo is available at https://zqwang7.github.io/demos/TF-GridNet-demo/index.htm