328 citations · 732 across the 8 of their papers we have counts for
8 papers
Ultra Low Complexity Deep Learning Based Noise Suppression
Shrishti Saha Shetu, Soumitro Chakrabarty, Oliver Thiergart +1
This paper introduces an innovative method for reducing the computational complexity of deep neural networks in real-time speech enhancement on resource-constrained devices. The pr…
New Insights on Target Speaker Extraction
Mohamed Elminshawi, Wolfgang Mack, Srikanth Raj Chetupalli +2
Speaker extraction (SE) aims to segregate the speech of a target speaker from a mixture of interfering speakers with the help of auxiliary information. Several forms of auxiliary i…
An Empirical Study of Visual Features for DNN based Audio-Visual Speech Enhancement in Multi-talker Environments
Shrishti Saha Shetu, Soumitro Chakrabarty, Emanuël A. P. Habets
Audio-visual speech enhancement (AVSE) methods use both audio and visual features for the task of speech enhancement and the use of visual features has been shown to be particularl…
Multi-scale aggregation of phase information for reducing computational cost of CNN based DOA estimation
Soumitro Chakrabarty, Emanuël A. P. Habets
In a recent work on direction-of-arrival (DOA) estimation of multiple speakers with convolutional neural networks (CNNs), the phase component of short-time Fourier transform (STFT)…
Multi-Speaker DOA Estimation Using Deep Convolutional Networks Trained with Noise Signals
Soumitro Chakrabarty, Emanuël A. P. Habets
Supervised learning based methods for source localization, being data driven, can be adapted to different acoustic conditions via training and have been shown to be robust to adver…
Multi-Speaker Localization Using Convolutional Neural Network Trained with Noise
Soumitro Chakrabarty, Emanuël A. P. Habets
The problem of multi-speaker localization is formulated as a multi-class multi-label classification problem, which is solved using a convolutional neural network (CNN) based source…