4 citations · 12 across the 13 of their papers we have counts for
13 papers
ExARN: self-attending RNN for target speaker extraction
Pengjie Shen, Shulin He, Xueliang Zhang
Target speaker extraction is to extract the target speaker, specified by enrollment utterance, in an environment with other competing speakers. Therefore, the task needs to solve t…
Speech Enhancement with Intelligent Neural Homomorphic Synthesis
Shulin He, Wei Rao, Jinjiang Liu +5
Most neural network speech enhancement models ignore speech production mathematical models by directly mapping Fourier transform spectrums or waveforms. In this work, we propose a…
Hierarchical speaker representation for target speaker extraction
Shulin He, Huaiwen Zhang, Wei Rao +4
Target speaker extraction aims to isolate a specific speaker's voice from a composite of multiple sound sources, guided by an enrollment utterance or called anchor. Current methods…
LCSM: A Lightweight Complex Spectral Mapping Framework for Stereophonic Acoustic Echo Cancellation
Chenggang Zhang, Jinjiang Liu, Xueliang Zhang
The traditional adaptive algorithms will face the non-uniqueness problem when dealing with stereophonic acoustic echo cancellation (SAEC). In this paper, we first propose an effici…
Inplace Gated Convolutional Recurrent Neural Network For Dual-channel Speech Enhancement
Jinjiang Liu, Xueliang Zhang
For dual-channel speech enhancement, it is a promising idea to design an end-to-end model based on the traditional array signal processing guideline and the manifold space of multi…
DBNet: A Dual-branch Network Architecture Processing on Spectrum and Waveform for Single-channel Speech Enhancement
Kanghao Zhang, Shulin He, Hao Li +1
In real acoustic environment, speech enhancement is an arduous task to improve the quality and intelligibility of speech interfered by background noise and reverberation. Over the…
Guided Training: A Simple Method for Single-channel Speaker Separation
Hao Li, Xueliang Zhang, Guanglai Gao
Deep learning has shown a great potential for speech separation, especially for speech and non-speech separation. However, it encounters permutation problem for multi-speaker separ…
Speakerfilter-Pro: an improved target speaker extractor combines the time domain and frequency domain
Shulin He, Hao Li, Xueliang Zhang
This paper introduces an improved target speaker extractor, referred to as Speakerfilter-Pro, based on our previous Speakerfilter model. The Speakerfilter uses a bi-direction gated…
Single Channel Speech Enhancement Using Temporal Convolutional Recurrent Neural Networks
Jingdong Li, Hui Zhang, Xueliang Zhang +1
In recent decades, neural network based methods have significantly improved the performace of speech enhancement. Most of them estimate time-frequency (T-F) representation of targe…
A Monaural Speech Enhancement Method for Robust Small-Footprint Keyword Spotting
Yue Gu, Zhihao Du, Hui Zhang +1
Robustness against noise is critical for keyword spotting (KWS) in real-world environments. To improve the robustness, a speech enhancement front-end is involved. Instead of treati…
Investigation of Monaural Front-End Processing for Robust ASR without Retraining or Joint-Training
Zhihao Du, Xueliang Zhang, Jiqing Han
In recent years, monaural speech separation has been formulated as a supervised learning problem, which has been systematically researched and shown the dramatical improvement of s…
Using Optimal Ratio Mask as Training Target for Supervised Speech Separation
Shasha Xia, Hao Li, Xueliang Zhang
Supervised speech separation uses supervised learning algorithms to learn a mapping from an input noisy signal to an output target. With the fast development of deep learning, supe…
Integrated Speech Enhancement Method Based on Weighted Prediction Error and DNN for Dereverberation and Denoising
Hao Li, Xueliang Zhang, Hui Zhang +1
Both reverberation and additive noises degrade the speech quality and intelligibility. Weighted prediction error (WPE) method performs well on the dereverberation but with limitati…