15 citations · 47 across the 10 of their papers we have counts for
10 papers
Toward Speech Separation in The Pre-Cocktail Party Problem with TasTas
Ziqiang Shi, Jiqing Han
In this note, we propose to use TasTas \cite{shi2020speech} for the end-to-end approach to monaural speech separation in the pre-cocktail party problem. Our experiments on the publ…
Speech Separation Based on Multi-Stage Elaborated Dual-Path Deep BiLSTM with Auxiliary Identity Loss
Ziqiang Shi, Rujie Liu, Jiqing Han
Deep neural network with dual-path bi-directional long short-term memory (BiLSTM) block has been proved to be very effective in sequence modeling, especially in speech separation.…
LaFurca: Iterative Refined Speech Separation Based on Context-Aware Dual-Path Parallel Bi-LSTM
Ziqiang Shi, Rujie Liu, Jiqing Han
Deep neural network with dual-path bi-directional long short-term memory (BiLSTM) block has been proved to be very effective in sequence modeling, especially in speech separation,…
FurcaNet: An end-to-end deep gated convolutional, long short-term memory, deep neural networks for single channel speech separation
Ziqiang Shi, Huibin Lin, Liu Liu +4
Deep gated convolutional networks have been proved to be very effective in single channel speech separation. However current state-of-the-art framework often considers training the…
Is CQT more suitable for monaural speech separation than STFT? an empirical study
Ziqiang Shi, Huibin Lin, Liu Liu +2
Short-time Fourier transform (STFT) is used as the front end of many popular successful monaural speech separation methods, such as deep clustering (DPCL), permutation invariant tr…
FurcaNeXt: End-to-end monaural speech separation with dynamic gated dilated temporal convolutional networks
Liwen Zhang, Ziqiang Shi, Jiqing Han +2
Deep dilated temporal convolutional networks (TCN) have been proved to be very effective in sequence modeling. In this paper we propose several improvements of TCN for end-to-end a…
Identifiability of multivariate logistic mixture models
ZiQiang Shi, TieRan Zheng, JiQing Han
Mixture models have been widely used in modeling of continuous observations. For the possibility to estimate the parameters of a mixture model consistently on the basis of observat…
Guarantees of Augmented Trace Norm Models in Tensor Recovery
Ziqiang Shi, Jiqing Han, Tieran Zheng +2
This paper studies the recovery guarantees of the models of minimizing where is a tensor and a…
Online Learning for Classification of Low-rank Representation Features and Its Applications in Audio Segment Classification
Ziqiang Shi, Jiqing Han, Tieran Zheng +1
In this paper, a novel framework based on trace norm minimization for audio segment is proposed. In this framework, both the feature extraction and classification are obtained by s…
Trace Norm Regularized Tensor Classification and Its Online Learning Approaches
Ziqiang Shi, Tieran Zheng, Jiqing Han
In this paper we propose an algorithm to classify tensor data. Our methodology is built on recent studies about matrix classification with the trace norm constrained weight matrix…