The decoupling-style concept begins to ignite in the speech enhancement area, which decouples the original complex spectrum estimation task into multiple easier sub-tasks ( i.e. , the magnitude-only recovery and residual complex spectrum estimation), resulting in better performance and easier interpretability. In this paper, we propose a dual-branch federative magnitude and phase estimation framework, dubbed DBT-Net, for monaural speech enhancement, aiming at recovering the coarse- and fine-grained regions of the overall spectrum in parallel. From the complementary perspective, the magnitude estimation branch is designed to filter out dominant noise components in the magnitude domain, while the complex spectrum purification branch is elaborately designed to inpaint the missing spectral details and implicitly estimate the phase information in the complex-valued spectral domain. To facilitate the information flow between each branch, interaction modules are introduced to leverage features learned from one branch, so as to suppress the undesired parts and recover the missing components of the other branch. Instead of adopting the conventional RNNs and temporal convolutional networks for sequence modeling, we employ a novel attention-in-attention transformer-based network within each branch for better feature learning. More specially, it is composed of several adaptive spectro-temporal attention transformer-based modules and an adaptive hierarchical attention module, aiming to capture long-term time-frequency dependencies and further aggregate intermediate hierarchical contextual information. Comprehensive evaluations on the WSJ0-SI84 + DNS-Challenge and VoiceBank + DEMAND dataset demonstrate that the proposed approach consistently outperforms previous advanced systems and yields state-of-the-art performance in terms of speech quality and intelligibility.

In low signal-to-noise ratio (SNR) acoustic scenarios, it remains fairly challenging to extract the target speech from its noisy mixture. In this paper, we propose a collaborative-style framework, namely, filtering and refining network (FRNet) for single-channel speech enhancement, recovering the complex spectrum of the target speech from coarse and fine-grained perspectives. Specifically, we devise a two-branch structure dubbed filtering-refining module (FRM). In the filtering block, the phase impact is ignored, and we only focus on coarse filtering in the magnitude domain. In the refining block, instead of predicting the irregular phase distribution directly, we estimate the complex residual for phase modification and spectrum rehabilitation, which takes the harmonic structure but with rather sparse energy distribution. By cascading FRMs repeatedly, we can reconstruct the target spectrum progressively. Furthermore, we propose a two-stream feature encoder to extract the feature representation of magnitude and phase individually, and the utilization of feature recalibration layers can preserve the prominent information from multiple scales. Extensive experiments are conducted on the WSJ0-SI84, Voicebank+Demand, and DNS-Challenge corpora. Evaluation results show that the proposed system performs favorably against previous advanced systems and achieves overall state-of-the-art performance in PESQ, ESTOI, SDR, and DNSMOS metrics.

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 with the motivation of 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.

Generative adversarial networks (GANs) have been increasingly used as feature mapping functions in speech enhancement, in which the noisy speech features are transformed to the clean ones through the generators. This paper proposes a novel speech enhancement model based on a cycle-consistent relativistic GAN with Dilated Residual Networks and a Multi-attention mechanism. Using the adversarial loss, improved cycle-consistency losses, and an identity-mapping loss, a noisy-to-clean generator G and an inverse clean-to-noisy generator F simultaneously learn the forward and backward mappings between the source and target domains. To guarantee the stability of the training process, we replace vanilla GAN loss with relativistic average GAN loss and use spectral normalization in discriminators so that they conform to Lipschitz continuity. Furthermore, we employ two attention-based components as multi-attention mechanism to reduce importing signal distortion: attention U-net gates and dilated residual self-attention blocks. By employing these components, our proposed generators can capture long-term inner dependencies between elements of speech features and further preserve linguistic information. Experimental results on a public dataset indicate that the proposed model achieves state-of-the-art speech enhancement performance, especially in reducing speech distortion and improving signal overall quality. Compared with the representative GAN-based approaches, the proposed method significantly achieves the best performance in terms of STOI, CSIG, COVL, and CBAK objective metrics. Moreover, we demonstrate the contribution of each proposed component including relativistic average loss, attention U-net gate, self-attention layers, spectral normalization, and dilation operation by ten comparison systems.

For the lack of adequate paired noisy-clean speech corpus in many real scenarios, non-parallel training is a promising task for DNN-based speech enhancement methods. However, because of the severe mismatch between input and target speeches, many previous studies only focus on the magnitude spectrum estimation and remain the phase unaltered, resulting in the degraded speech quality under low signal-to-noise ratio conditions. To tackle this problem, we decouple the difficult target w.r.t. original spectrum optimization into spectral magnitude and phase, and a novel Cycle-in-Cycle generative adversarial network (dubbed CinCGAN) is proposed to jointly estimate the spectral magnitude and phase information stage by stage under unpaired data. In the first stage, we pretrain a magnitude CycleGAN to coarsely estimate the spectral magnitude of clean speech. In the second stage, we incorporate the pretrained CycleGAN with a complex-valued CycleGAN as a cycle-in-cycle structure to simultaneously recover phase information and refine the overall spectrum. Experimental results demonstrate that the proposed approach significantly outperforms previous baselines under non-parallel training. The evaluation on training the models with standard paired data also shows that CinCGAN achieves remarkable performance especially in reducing background noise and speech distortion.