18 citations · 119 across the 25 of their papers we have counts for
8 papers · 1 filter
Acoustic Scene Classification by Implicitly Identifying Distinct Sound Events
Hongwei Song, Jiqing Han, Shiwen Deng +1
In this paper, we propose a new strategy for acoustic scene classification (ASC) , namely recognizing acoustic scenes through identifying distinct sound events. This differs from e…
A Multi-Task Learning Framework for Overcoming the Catastrophic Forgetting in Automatic Speech Recognition
Jiabin Xue, Jiqing Han, Tieran Zheng +2
Recently, data-driven based Automatic Speech Recognition (ASR) systems have achieved state-of-the-art results. And transfer learning is often used when those existing systems are a…
Hard Sample Mining for the Improved Retraining of Automatic Speech Recognition
Jiabin Xue, Jiqing Han, Tieran Zheng +2
It is an effective way that improves the performance of the existing Automatic Speech Recognition (ASR) systems by retraining with more and more new training data in the target dom…
A Compact and Discriminative Feature Based on Auditory Summary Statistics for Acoustic Scene Classification
Hongwei Song, Jiqing Han, Shiwen Deng
One of the biggest challenges of acoustic scene classification (ASC) is to find proper features to better represent and characterize environmental sounds. Environmental sounds gene…
Acoustic scene classification using multi-layer temporal pooling based on convolutional neural network
Liwen Zhang, Jiqing Han
The performance of an Acoustic Scene Classification (ASC) system is highly depending on the latent temporal dynamics of the audio signal. In this paper, we proposed a multiple laye…
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…