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Wenju Liu

8 accepted papers

2023

GCC-Speaker: Target Speaker Localization with Optimal Speaker-Dependent Weighting in Multi-Speaker Scenarios

ICASSP 2023accepted

Existing noise-robust and reverberant-robust localization algorithms fail to localize the target speaker when interfering speakers are present. In this paper, we address the problem of localizing only the target speaker in multi-speaker scenarios and propose a target speaker localization algorithm,…

Cited by 0SourceScholar
2019

Adaptive Dereverberation Using Multi-channel Linear Prediction with Deficient Length Filter

ICASSP 2019accepted

In almost all adaptive dereverberation algorithms based on the multi-channel linear prediction (MCLP) model, it is assumed that the filter length can cover the reverberation time. However, in many practical situations, a deficient length filter, whose length is less than the reverberation time, is e…

Cited by 0SourceScholar
2019

Loss and Double-edge-triggered Detector for Robust Small-footprint Keyword Spotting

ICASSP 2019accepted

Keyword spotting (KWS) system constitutes a critical component of human-computer interfaces, which detects the specific keyword from a continuous stream of audio. The goal of KWS is providing a high detection accuracy at a low false alarm rate while having small memory and computation requirements.…

Cited by 17SourceScholar
2019

Sequence-To-Sequence Domain Adaptation Network for Robust Text Image Recognition

CVPR 2019poster

Domain adaptation has shown promising advances for alleviating domain shift problem. However, recent visual domain adaptation works usually focus on non-sequential object recognition with a global coarse alignment, which is inadequate to transfer effective knowledge for sequence-like text images wit…

Cited by 163PDFScholar
2018

Boosting Noise Robustness of Acoustic Model via Deep Adversarial Training

ICASSP 2018accepted

In realistic environments, speech is usually interfered by various noise and reverberation, which dramatically degrades the performance of automatic speech recognition (ASR) systems. To alleviate this issue, the commonest way is to use a well-designed speech enhancement approach as the front-end of…

Cited by 0SourceScholar
2016

Exploiting spectro-temporal structures using NMF for DNN-based supervised speech separation

ICASSP 2016accepted

The targets of speech separation, whether ideal masks or magnitude spectrograms of interest, have prominent spectro-temporal structures. These characteristics are very worthy to be exploited for speech separation, however, they are usually ignored in previous works. In this paper, we use nonnegative…

Cited by 0SourceScholar
2015

A pairwise algorithm for pitch estimation and speech separation using deep stacking network

ICASSP 2015accepted

Pitch information is an important cue for speech separation. However, pitch estimation in noisy condition is also a task as challenging as speech separation. In this paper, we propose a supervised learning architecture which combines these two problems concisely. The proposed algorithm is based on d…

Cited by 0SourceScholar
2015

Cross-domain cooperative deep stacking network for speech separation

ICASSP 2015accepted

Nowadays supervised speech separation has drawn much attention and shown great promise in the meantime. While there has been a lot of success, existing algorithms perform the task only in one preselected representative domain. In this study, we propose to perform the task in two different time-frequ…

Cited by 0SourceScholar