SOTAVerified

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

Showing 851900 of 982 papers

TitleStatusHype
Deep learning for minimum mean-square error approaches to speech enhancement0
My lips are concealed: Audio-visual speech enhancement through obstructions0
Convolutional Neural Network-based Speech Enhancement for Cochlear Implant Recipients0
A Monaural Speech Enhancement Method for Robust Small-Footprint Keyword Spotting0
The Second DIHARD Diarization Challenge: Dataset, task, and baselinesCode0
rVAD: An Unsupervised Segment-Based Robust Voice Activity Detection MethodCode0
Increasing Compactness Of Deep Learning Based Speech Enhancement Models With Parameter Pruning And Quantization Techniques0
Guided Source Separation Meets a Strong ASR Backend: Hitachi/Paderborn University Joint Investigation for Dinner Party ASRCode0
Deep-Learning-Based Audio-Visual Speech Enhancement in Presence of Lombard Effect0
A Perceptual Weighting Filter Loss for DNN Training in Speech EnhancementCode0
DNN-Based Speech Presence Probability Estimation for Multi-Frame Single-Microphone Speech Enhancement0
Universal Sound Separation0
Learning with Learned Loss Function: Speech Enhancement with Quality-Net to Improve Perceptual Evaluation of Speech QualityCode0
A Statistically Principled and Computationally Efficient Approach to Speech Enhancement using Variational Autoencoders0
DEEP COMPLEX-VALUED NEURAL BEAMFORMERSCode0
Perceptually-motivated Environment-specific Speech Enhancement0
Incorporating Symbolic Sequential Modeling for Speech Enhancement0
Multi-Geometry Spatial Acoustic Modeling for Distant Speech Recognition0
Frequency Domain Multi-channel Acoustic Modeling for Distant Speech Recognition0
An Investigation of End-to-End Multichannel Speech Recognition for Reverberant and Mismatch Conditions0
An Analysis of Speech Enhancement and Recognition Losses in Limited Resources Multi-talker Single Channel Audio-Visual ASR0
RHR-Net: A Residual Hourglass Recurrent Neural Network for Speech EnhancementCode0
VoiceID Loss: Speech Enhancement for Speaker Verification0
Taco-VC: A Single Speaker Tacotron based Voice Conversion with Limited Data0
Towards Generalized Speech Enhancement with Generative Adversarial Networks0
Speech denoising by parametric resynthesis0
Unsupervised Speech Enhancement Based on Multichannel NMF-Informed Beamforming for Noise-Robust Automatic Speech Recognition0
Bridging the Gap Between Monaural Speech Enhancement and Recognition with Distortion-Independent Acoustic Modeling0
An improved uncertainty propagation method for robust i-vector based speaker recognition0
Speech enhancement with variational autoencoders and alpha-stable distributions0
A variance modeling framework based on variational autoencoders for speech enhancementCode0
An Ensemble SVM-based Approach for Voice Activity Detection0
End-to-End Multi-Task Denoising for joint SDR and PESQ OptimizationCode0
Deep Speech Enhancement for Reverberated and Noisy Signals using Wide Residual Networks0
End-to-End Model for Speech Enhancement by Consistent Spectrogram Masking0
Tensor-Train Long Short-Term Memory for Monaural Speech Enhancement0
Acoustics-guided evaluation (AGE): a new measure for estimating performance of speech enhancement algorithms for robust ASR0
Improved Speech Enhancement with the Wave-U-NetCode0
Analysis of DNN Speech Signal Enhancement for Robust Speaker Recognition0
Using recurrences in time and frequency within U-net architecture for speech enhancement0
Semi-supervised multichannel speech enhancement with variational autoencoders and non-negative matrix factorization0
Effects of Lombard Reflex on the Performance of Deep-Learning-Based Audio-Visual Speech Enhancement Systems0
On Training Targets and Objective Functions for Deep-Learning-Based Audio-Visual Speech Enhancement0
Reinforcement Learning Based Speech Enhancement for Robust Speech Recognition0
Speech Enhancement Based on Reducing the Detail Portion of Speech Spectrograms in Modulation Domain via Discrete Wavelet TransformCode0
Kernel Machines Beat Deep Neural Networks on Mask-based Single-channel Speech Enhancement0
Unpaired Speech Enhancement by Acoustic and Adversarial Supervision for Speech RecognitionCode0
Face Landmark-based Speaker-Independent Audio-Visual Speech Enhancement in Multi-Talker EnvironmentsCode0
Trainable Adaptive Window Switching for Speech Enhancement0
ConvS2S-VC: Fully convolutional sequence-to-sequence voice conversion0
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Benchmark Results

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