1 citations · 1 across the 5 of their papers we have counts for
5 papers
Distil-DCCRN: A Small-footprint DCCRN Leveraging Feature-based Knowledge Distillation in Speech Enhancement
Runduo Han, Weiming Xu, Zihan Zhang +2
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…
WenetSpeech4TTS: A 12,800-hour Mandarin TTS Corpus for Large Speech Generation Model Benchmark
Linhan Ma, Dake Guo, Kun Song +7
With the development of large text-to-speech (TTS) models and scale-up of the training data, state-of-the-art TTS systems have achieved impressive performance. In this paper, we pr…
An audio-quality-based multi-strategy approach for target speaker extraction in the MISP 2023 Challenge
Runduo Han, Xiaopeng Yan, Weiming Xu +6
This paper describes our audio-quality-based multi-strategy approach for the audio-visual target speaker extraction (AVTSE) task in the Multi-modal Information based Speech Process…
MBTFNet: Multi-Band Temporal-Frequency Neural Network For Singing Voice Enhancement
Weiming Xu, Zhouxuan Chen, Zhili Tan +5
A typical neural speech enhancement (SE) approach mainly handles speech and noise mixtures, which is not optimal for singing voice enhancement scenarios. Music source separation (M…
TaylorAECNet: A Taylor Style Neural Network for Full-Band Echo Cancellation
Weiming Xu, Zhihao Guo
This paper describes aecX team's entry to the ICASSP 2023 acoustic echo cancellation (AEC) challenge. Our system consists of an adaptive filter and a proposed full-band Taylor-styl…