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Speech Enhancement

Speech Enhancement is a signal processing task that involves improving the quality of speech signals captured under noisy or degraded conditions. The goal of speech enhancement is to make speech signals clearer, more intelligible, and more pleasant to listen to, which can be used for various applications such as voice recognition, teleconferencing, and hearing aids. A representative Github project with online demo : ClearerVoice-Studio.

( Image credit: A Fully Convolutional Neural Network For Speech Enhancement )

Papers

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Showing 201–250 of 982 papers

TitleStatusHype
Exploration of Adapter for Noise Robust Automatic Speech Recognition—0
Audio-Visual Speech Enhancement in Noisy Environments via Emotion-Based Contextual Cues—0
Self-Supervised Speech Quality Estimation and Enhancement Using Only Clean SpeechCode2
SICRN: Advancing Speech Enhancement through State Space Model and Inplace Convolution Techniques—0
Mel-FullSubNet: Mel-Spectrogram Enhancement for Improving Both Speech Quality and ASR—0
Plugin Speech Enhancement: A Universal Speech Enhancement Framework Inspired by Dynamic Neural Network—0
SECP: A Speech Enhancement-Based Curation Pipeline For Scalable Acquisition Of Clean Speech—0
Speaking in Wavelet Domain: A Simple and Efficient Approach to Speed up Speech Diffusion Model—0
Diffusion Models for Audio Restoration—0
Overview of the L3DAS23 Challenge on Audio-Visual Extended Reality—0
Unrestricted Global Phase Bias-Aware Single-channel Speech Enhancement with Conformer-based Metric GAN—0
Array Geometry-Robust Attention-Based Neural Beamformer for Moving Speakers—0
Objective and subjective evaluation of speech enhancement methods in the UDASE task of the 7th CHiME challengeCode0
Real-time Stereo Speech Enhancement with Spatial-Cue Preservation based on Dual-Path Structure—0
An Analysis of the Variance of Diffusion-based Speech Enhancement—0
SpeechComposer: Unifying Multiple Speech Tasks with Prompt Composition—0
Improving Design of Input Condition Invariant Speech EnhancementCode0
A Two-Stage Framework in Cross-Spectrum Domain for Real-Time Speech Enhancement—0
An Empirical Study on the Impact of Positional Encoding in Transformer-based Monaural Speech Enhancement—0
On Speech Pre-emphasis as a Simple and Inexpensive Method to Boost Speech Enhancement—0
Noise-robust zero-shot text-to-speech synthesis conditioned on self-supervised speech-representation model with adapters—0
FADI-AEC: Fast Score Based Diffusion Model Guided by Far-end Signal for Acoustic Echo Cancellation—0
A unified multichannel far-field speech recognition system: combining neural beamforming with attention based end-to-end model—0
Single-channel speech enhancement using learnable loss mixup—0
On real-time multi-stage speech enhancement systems—0
A Refining Underlying Information Framework for Monaural Speech EnhancementCode1
Attention-Driven Multichannel Speech Enhancement in Moving Sound Source Scenarios—0
A Deep Representation Learning-based Speech Enhancement Method Using Complex Convolution Recurrent Variational Autoencoder—0
SELM: Speech Enhancement Using Discrete Tokens and Language Models—0
Ultra Low Complexity Deep Learning Based Noise Suppression—0
ROSE: A Recognition-Oriented Speech Enhancement Framework in Air Traffic Control Using Multi-Objective LearningCode0
Investigating the Design Space of Diffusion Models for Speech EnhancementCode1
Diffusion-Based Speech Enhancement in Matched and Mismatched Conditions Using a Heun-Based Sampler—0
SEFGAN: Harvesting the Power of Normalizing Flows and GANs for Efficient High-Quality Speech Enhancement—0
Head Orientation Estimation with Distributed Microphones Using Speech Radiation Patterns—0
Subspace Hybrid MVDR Beamforming for Augmented Hearing—0
D4AM: A General Denoising Framework for Downstream Acoustic ModelsCode1
LC4SV: A Denoising Framework Learning to Compensate for Unseen Speaker Verification Models—0
CheapNET: Improving Light-weight speech enhancement network by projected loss function—0
Cooperative Dual Attention for Audio-Visual Speech Enhancement with Facial Cues—0
Sparsity-Driven EEG Channel Selection for Brain-Assisted Speech Enhancement—0
How does end-to-end speech recognition training impact speech enhancement artifacts?—0
SE Territory: Monaural Speech Enhancement Meets the Fixed Virtual Perceptual Space Mapping—0
Scenario-Aware Audio-Visual TF-GridNet for Target Speech Extraction—0
DPATD: Dual-Phase Audio Transformer for Denoising—0
TorchAudio 2.1: Advancing speech recognition, self-supervised learning, and audio processing components for PyTorchCode4
Single channel speech enhancement by colored spectrograms—0
Generative Pre-training for Speech with Flow Matching—0
LC-TTFS: Towards Lossless Network Conversion for Spiking Neural Networks with TTFS Coding—0
Deep Beamforming for Speech Enhancement and Speaker Localization with an Array Response-Aware Loss Function—0
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All datasetsVoiceBank + DEMANDDeep Noise Suppression (DNS) ChallengeCHiME-3EARS-WHAMEasyComDNS ChallengeVB-DemandExWHAMR!WSJ0 + DEMAND + RNNoiseRealMANVoiceBank+DEMANDDEMAND

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ROSE-CD(PESQ)PESQ (wb)3.99—Unverified
2PESQetarianPESQ (wb)3.82—Unverified
3Mamba-SEUNet L (+PCS)PESQ (wb)3.73—Unverified
4Schrödinger bridge (PESQ loss)PESQ (wb)3.7—Unverified
5SEMamba (+PCS)PESQ (wb)3.69—Unverified
6ZipEnhancer (S, \lamba_6 = 0)PESQ (wb)3.63—Unverified
7PrimeK-NetPESQ (wb)3.61—Unverified
8ZipEnhancer (S, \lamba_6 = 0.2)PESQ (wb)3.61—Unverified
9MP-SENetPESQ (wb)3.6—Unverified
10PCS_CS_WAVLMPESQ (wb)3.54—Unverified
#ModelMetricClaimedVerifiedStatus
1BSRNN-S + MGDSI-SDR-WB21.4—Unverified
2DTLNSI-SDR-WB16.34—Unverified
3Non-Real-Time MultiScale+SI-SDR-WB16.22—Unverified
4ZipEnhancer (M)PESQ-WB3.81—Unverified
5TF-Locoformer (M)PESQ-WB3.72—Unverified
6ZipEnhancer (S)PESQ-WB3.69—Unverified
7MambAttentionPESQ-WB3.67—Unverified
8MP-SENetPESQ-WB3.62—Unverified
9xLSTM-SENetPESQ-WB3.59—Unverified
10BSRNN-S + MRSDPESQ-WB3.53—Unverified
#ModelMetricClaimedVerifiedStatus
1Inter-Channel Conv-TasNetSDR19.67—Unverified
2CA Dense U-Net (Complex)SDR18.64—Unverified
3Dense U-Net (Complex)SDR18.4—Unverified
4Dense U-Net (Real)SDR16.86—Unverified
5U-Net (Real)SDR15.97—Unverified
6Noisy/unprocessedSDR6.5—Unverified
#ModelMetricClaimedVerifiedStatus
1Schrödinger Bridge (PESQ loss)PESQ-WB3.09—Unverified
2SGMSE+PESQ-WB2.5—Unverified
3Demucs v4PESQ-WB2.37—Unverified
4Schrödinger BridgePESQ-WB2.33—Unverified
5Conv-TasNetPESQ-WB2.31—Unverified
6CDiffuSEPESQ-WB1.6—Unverified
#ModelMetricClaimedVerifiedStatus
1ReVISE (ch2)Audio Quality MOS4.19—Unverified
2ReVISE (bf)Audio Quality MOS4.11—Unverified
3Demucs (ch2)Audio Quality MOS2.95—Unverified
4Demucs (bf)Audio Quality MOS2.39—Unverified
5MaxDI (Baseline)PESQ1.17—Unverified
6DAJA (MVDR,HMA,1000) (Overlapped Speech)SDR-4.76—Unverified
#ModelMetricClaimedVerifiedStatus
1ZipEnhancer (M)PESQ-NB4.08—Unverified
2DCCRN-MCPESQ-NB3.21—Unverified
3DCCRN-MPESQ-NB3.15—Unverified
4DCCRNPESQ-NB3.04—Unverified
5RNN-ModulationPESQ-WB2.75—Unverified
#ModelMetricClaimedVerifiedStatus
1MambAttentionESTOI0.8—Unverified
2SEMambaESTOI0.8—Unverified
3xLSTM-SENetESTOI0.8—Unverified
4MP-SENetESTOI0.79—Unverified
#ModelMetricClaimedVerifiedStatus
1SepFormerPESQ2.84—Unverified
2DTLNPESQ2.23—Unverified
3UnprocessedPESQ1.83—Unverified
4Non-Real-Time MultiScale+PESQ1.52—Unverified
#ModelMetricClaimedVerifiedStatus
1DCUNet-MCPESQ-NB3.44—Unverified
2DCCRN-MPESQ-NB3.28—Unverified
3DCUNetPESQ-NB3.25—Unverified
#ModelMetricClaimedVerifiedStatus
1CleanMel-L-mapDNSMOS3.82—Unverified
2SpatialNetDNSMOS BAK3.43—Unverified
#ModelMetricClaimedVerifiedStatus
1rose_cd(PESQ )PESQ3.99—Unverified
2ROSE-CDPESQ3.49—Unverified
#ModelMetricClaimedVerifiedStatus
1Wave-U-NetCBAK3.24—Unverified
#ModelMetricClaimedVerifiedStatus
1Audio-Visual concat-refPESQ2.7—Unverified
#ModelMetricClaimedVerifiedStatus
1SE-MelGANAudio Quality MOS3.1—Unverified
#ModelMetricClaimedVerifiedStatus
1DeFT-ANPESQ3.01—Unverified
#ModelMetricClaimedVerifiedStatus
1Audio-Visual concat-refPESQ3.03—Unverified
#ModelMetricClaimedVerifiedStatus
1SepFormerPESQ3.07—Unverified