Search Results for author:
Found 8 papers, 2 papers with code
MBTFNet: Multi-Band Temporal-Frequency Neural Network For Singing Voice Enhancement
A typical neural speech enhancement (SE) approach mainly handles speech and noise mixtures, which is not optimal for singing voice enhancement scenarios.
DCCRN-KWS: an audio bias based model for noise robust small-footprint keyword spotting
Real-world complex acoustic environments especially the ones with a low signal-to-noise ratio (SNR) will bring tremendous challenges to a keyword spotting (KWS) system.
Two-stage Neural Network for ICASSP 2023 Speech Signal Improvement Challenge
Achieving 0. 446 in the final score and 0. 517 in the P. 835 score, our system ranks 4th in the non-real-time track.
spatial-dccrn: dccrn equipped with frame-level angle feature and hybrid filtering for multi-channel speech enhancement
Recently, multi-channel speech enhancement has drawn much interest due to the use of spatial information to distinguish target speech from interfering signal.
Uformer: A Unet based dilated complex & real dual-path conformer network for simultaneous speech enhancement and dereverberation
Complex spectrum and magnitude are considered as two major features of speech enhancement and dereverberation.
DCCRN+: Channel-wise Subband DCCRN with SNR Estimation for Speech Enhancement
Deep complex convolution recurrent network (DCCRN), which extends CRN with complex structure, has achieved superior performance in MOS evaluation in Interspeech 2020 deep noise suppression challenge (DNS2020).
DCCRN: Deep Complex Convolution Recurrent Network for Phase-Aware Speech Enhancement
Speech enhancement has benefited from the success of deep learning in terms of intelligibility and perceptual quality.
Ranked #14 on
Speech Enhancement
on Deep Noise Suppression (DNS) Challenge
(PESQ-NB metric)
Speech Enhancement
Audio and Speech Processing
Sound
