Speech Denoising
27 papers with code • 2 benchmarks • 3 datasets
Obtain the clean speech of the target speaker by suppressing the background noise.
Benchmarks
These leaderboards are used to track progress in Speech Denoising
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EmoSpeech
WHAMR_ext
Most implemented papers
WaveCRN: An Efficient Convolutional Recurrent Neural Network for End-to-end Speech Enhancement
In WaveCRN, the speech locality feature is captured by a convolutional neural network (CNN), while the temporal sequential property of the locality feature is modeled by stacked simple recurrent units (SRU).
Sparse Mixture of Local Experts for Efficient Speech Enhancement
In this paper, we investigate a deep learning approach for speech denoising through an efficient ensemble of specialist neural networks.
Phase-aware Single-stage Speech Denoising and Dereverberation with U-Net
In this work, we tackle a denoising and dereverberation problem with a single-stage framework.
Listening to Sounds of Silence for Speech Denoising
henryxrl/Listening-to-Sound-of-Silence-for-Speech-Denoising
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NeurIPS 2020
We introduce a deep learning model for speech denoising, a long-standing challenge in audio analysis arising in numerous applications.
Perceptual Loss based Speech Denoising with an ensemble of Audio Pattern Recognition and Self-Supervised Models
Using auxiliary models one at a time, we find acoustic event and self-supervised model PASE+ to be most effective.
Speech Denoising with Auditory Models
msaddler/auditory-model-denoising
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Contemporary speech enhancement predominantly relies on audio transforms that are trained to reconstruct a clean speech waveform.
Visual Speech Enhancement Without A Real Visual Stream
Sindhu-Hegde/pseudo-visual-speech-denoising
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In this work, we re-think the task of speech enhancement in unconstrained real-world environments.
A Modulation-Domain Loss for Neural-Network-based Real-time Speech Enhancement
tvuong123/ModulationDomainLoss
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We describe a modulation-domain loss function for deep-learning-based speech enhancement systems.
Self-Supervised Speech Denoising Using Only Noisy Audio Signals
liqingchunnnn/only-noisy-training
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The first module adopts a random audio sub-sampler on each noisy audio to generate training pairs.
Task-specific Optimization of Virtual Channel Linear Prediction-based Speech Dereverberation Front-End for Far-Field Speaker Verification
Developing a single-microphone speech denoising or dereverberation front-end for robust automatic speaker verification (ASV) in noisy far-field speaking scenarios is challenging.
