Search Results for author:
Found 7 papers, 1 papers with code
Rethinking complex-valued deep neural networks for monaural speech enhancement
By comparing complex- and real-valued versions of fundamental building blocks in the recently developed gated convolutional recurrent network (GCRN), we show how different mechanisms for basic blocks affect the performance.
Leveraging Heteroscedastic Uncertainty in Learning Complex Spectral Mapping for Single-channel Speech Enhancement
During training, our approach augments a model learning complex spectral mapping with a temporary submodel to predict the covariance of the enhancement error at each time-frequency bin.
Location-based training for multi-channel talker-independent speaker separation
We further demonstrate the effectiveness of LBT for the separation of four and five concurrent speakers.
SAGRNN: Self-Attentive Gated RNN for Binaural Speaker Separation with Interaural Cue Preservation
In addition, our approach effectively preserves the interaural cues, which improves the accuracy of sound localization.
Audio and Speech Processing Sound
Audio-Visual Speech Separation and Dereverberation with a Two-Stage Multimodal Network
Background noise, interfering speech and room reverberation frequently distort target speech in real listening environments.
Audio and Speech Processing Sound Signal Processing
Bridging the Gap Between Monaural Speech Enhancement and Recognition with Distortion-Independent Acoustic Modeling
In this study, we analyze the distortion problem, compare different acoustic models, and investigate a distortion-independent training scheme for monaural speech recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Deep Learning Based Phase Reconstruction for Speaker Separation: A Trigonometric Perspective
This study investigates phase reconstruction for deep learning based monaural talker-independent speaker separation in the short-time Fourier transform (STFT) domain.
