Distil-DCCRN: A Small-footprint DCCRN Leveraging Feature-based Knowledge Distillation in Speech Enhancement

The deep complex convolution recurrent network (DCCRN) achieves excellent speech enhancement performance by utilizing the audio spectrum's complex features. However, it has a large number of model parameters. We propose a smaller model, Distil-DCCRN, which has only 30% of the parameters compared to the DCCRN. To ensure that the performance of Distil-DCCRN matches that of the DCCRN, we employ the knowledge distillation (KD) method to use a larger teacher model to help train a smaller student model. We design a knowledge distillation (KD) method, integrating attention transfer and Kullback-Leibler divergence (AT-KL) to train the student model Distil-DCCRN. Additionally, we use a model with better performance and a more complicated structure, Uformer, as the teacher model. Unlike previous KD approaches that mainly focus on model outputs, our method also leverages the intermediate features from the models' middle layers, facilitating rich knowledge transfer across different structured models despite variations in layer configurations and discrepancies in the channel and time dimensions of intermediate features. Employing our AT-KL approach, Distil-DCCRN outperforms DCCRN as well as several other competitive models in both PESQ and SI-SNR metrics on the DNS test set and achieves comparable results to DCCRN in DNSMOS.

RaD-Net 2: A causal two-stage repairing and denoising speech enhancement network with knowledge distillation and complex axial self-attention

In real-time speech communication systems, speech signals are often degraded by multiple distortions. Recently, a two-stage Repair-and-Denoising network (RaD-Net) was proposed with superior speech quality improvement in the ICASSP 2024 Speech Signal Improvement (SSI) Challenge. However, failure to use future information and constraint receptive field of convolution layers limit the system's performance. To mitigate these problems, we extend RaD-Net to its upgraded version, RaD-Net 2. Specifically, a causality-based knowledge distillation is introduced in the first stage to use future information in a causal way. We use the non-causal repairing network as the teacher to improve the performance of the causal repairing network. In addition, in the second stage, complex axial self-attention is applied in the denoising network's complex feature encoder/decoder. Experimental results on the ICASSP 2024 SSI Challenge blind test set show that RaD-Net 2 brings 0.10 OVRL DNSMOS improvement compared to RaD-Net.

RaD-Net: A Repairing and Denoising Network for Speech Signal Improvement

This paper introduces our repairing and denoising network (RaD-Net) for the ICASSP 2024 Speech Signal Improvement (SSI) Challenge. We extend our previous framework based on a two-stage network and propose an upgraded model. Specifically, we replace the repairing network with COM-Net from TEA-PSE. In addition, multi-resolution discriminators and multi-band discriminators are adopted in the training stage. Finally, we use a three-step training strategy to optimize our model. We submit two models with different sets of parameters to meet the RTF requirement of the two tracks. According to the official results, the proposed systems rank 2nd in track 1 and 3rd in track 2.

Two-stage Neural Network for ICASSP 2023 Speech Signal Improvement Challenge

In ICASSP 2023 speech signal improvement challenge, we developed a dual-stage neural model which improves speech signal quality induced by different distortions in a stage-wise divide-and-conquer fashion. Specifically, in the first stage, the speech improvement network focuses on recovering the missing components of the spectrum, while in the second stage, our model aims to further suppress noise, reverberation, and artifacts introduced by the first-stage model. Achieving 0.446 in the final score and 0.517 in the P.835 score, our system ranks 4th in the non-real-time track.

Two-step Band-split Neural Network Approach for Full-band Residual Echo Suppression

This paper describes a Two-step Band-split Neural Network (TBNN) approach for full-band acoustic echo cancellation. Specifically, after linear filtering, we split the full-band signal into wide-band (16KHz) and high-band (16-48KHz) for residual echo removal with lower modeling difficulty. The wide-band signal is processed by an updated gated convolutional recurrent network (GCRN) with U$^2$ encoder while the high-band signal is processed by a high-band post-filter net with lower complexity. Our approach submitted to ICASSP 2023 AEC Challenge has achieved an overall mean opinion score (MOS) of 4.344 and a word accuracy (WAcc) ratio of 0.795, leading to the 2$^{nd}$ (tied) in the ranking of the non-personalized track.