7 accepted papers
Deep learning (DL)-based speaker diarization methods have proven powerful performance comparing to traditional clustering-based methods for multi-talker speech diarization and recognition in farfield scenes. However, most DL-based approaches cannot utilize the spatial information well due to the poo…
The recent criteria of a preferable linear sparse array for a robust direction of arrival (DOA) estimation are the closed-form expression for its sensor locations, the large central uniform linear array (ULA) segment in its resulting co-array and the fewer sensor pairs with small separations in its…
In recent years, the deep learning-based approaches are popular in the field of singe-channel speech enhancement. Convolutional neural networks (CNNs) are a standard component of many current speech enhancement system. In this study, we design a new Fully CNN (FCNN)-based regression model, which can…
The spectral information of acoustic scenes is diverse and complex, which poses challenges for acoustic scene tasks. To improve the classification performance, a variety of convolutional neural networks (CNNs) are proposed to extract richer semantic information of scene utterances. However, the diff…
For conventional single-channel speech enhancement based on noise power spectrum, the speech gain function, which suppresses background noise at each time-frequency bin, is calculated by prior signal-to-noise-ratio (SNR). Hence, accurate prior SNR estimation is paramount for successful noise suppres…
Conventional speech-enhancement techniques employ statistical signal-processing algorithms. They are computationally efficient and improve speech quality even under unknown noise conditions. For these reasons, they are preferred for deployment in unpredictable environments. One limitation of these a…
In this paper, we propose a new signal-noise-dependent (SND) deep neural network (DNN) framework to further improve the separation and recognition performance of the recently developed technique for general DNN-based speech separation. We adopt a divide and conquer strategy to design the proposed SN…