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 201250 of 982 papers

TitleStatusHype
Unifying Speech Enhancement and Separation with Gradient Modulation for End-to-End Noise-Robust Speech SeparationCode1
Spleeter: A Fast And State-of-the Art Music Source Separation Tool With Pre-trained ModelsCode1
Supervised and Unsupervised Speech Enhancement Using Nonnegative Matrix FactorizationCode0
Adversarial Privacy Protection on Speech EnhancementCode0
Deep Multi-Frame MVDR Filtering for Single-Microphone Speech EnhancementCode0
Speech Enhancement with Zero-Shot Model SelectionCode0
Word-level Embeddings for Cross-Task Transfer Learning in Speech ProcessingCode0
Speech Enhancement for Virtual Meetings on Cellular NetworksCode0
Speech Enhancement and Dereverberation with Diffusion-based Generative ModelsCode0
Speech Enhancement based on Denoising Autoencoder with Multi-branched EncodersCode0
Speech Enhancement with Overlapped-Frame Information Fusion and Causal Self-AttentionCode0
The Effect of Spoken Language on Speech Enhancement using Self-Supervised Speech Representation Loss FunctionsCode0
Speech Denoising Convolutional Neural Network trained with Deep Feature Losses.Code0
DEEP COMPLEX-VALUED NEURAL BEAMFORMERSCode0
Single Channel Speech Enhancement Using U-Net Spiking Neural NetworksCode0
Sparse Mixture of Local Experts for Efficient Speech EnhancementCode0
Self-Supervised Learning for Speech Enhancement through SynthesisCode0
A Perceptual Weighting Filter Loss for DNN Training in Speech EnhancementCode0
Self-Supervised Learning from Contrastive Mixtures for Personalized Speech EnhancementCode0
Speech-enhanced and Noise-aware Networks for Robust Speech RecognitionCode0
The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Speech Quality and Testing FrameworkCode0
Room Impulse Response Estimation through Optimal Mass Transport BarycentersCode0
ROSE: A Recognition-Oriented Speech Enhancement Framework in Air Traffic Control Using Multi-Objective LearningCode0
RHR-Net: A Residual Hourglass Recurrent Neural Network for Speech EnhancementCode0
Attention-based multi-task learning for speech-enhancement and speaker-identification in multi-speaker dialogue scenarioCode0
rVAD: An Unsupervised Segment-Based Robust Voice Activity Detection MethodCode0
Receptive Field Analysis of Temporal Convolutional Networks for Monaural Speech DereverberationCode0
Analysing Diffusion-based Generative Approaches versus Discriminative Approaches for Speech RestorationCode0
A Training and Inference Strategy Using Noisy and Enhanced Speech as Target for Speech Enhancement without Clean SpeechCode0
PL-EESR: Perceptual Loss Based END-TO-END Robust Speaker Representation ExtractionCode0
Phase-aware Single-stage Speech Denoising and Dereverberation with U-NetCode0
Exploring Self-Attention Mechanisms for Speech SeparationCode0
Contaminated speech training methods for robust DNN-HMM distant speech recognitionCode0
MMTM: Multimodal Transfer Module for CNN FusionCode0
Complex Recurrent Variational Autoencoder with Application to Speech EnhancementCode0
Masks Fusion with Multi-Target Learning For Speech EnhancementCode0
Objective and subjective evaluation of speech enhancement methods in the UDASE task of the 7th CHiME challengeCode0
The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Testing Framework, and Challenge ResultsCode0
Learning with Learned Loss Function: Speech Enhancement with Quality-Net to Improve Perceptual Evaluation of Speech QualityCode0
Lessons Learned from the URGENT 2024 Speech Enhancement ChallengeCode0
Speech Enhancement Based on Reducing the Detail Portion of Speech Spectrograms in Modulation Domain via Discrete Wavelet TransformCode0
Deep learning for minimum mean-square error approaches to speech enhancementCode0
Let SSMs be ConvNets: State-space Modeling with Optimal Tensor ContractionsCode0
Investigating Training Objectives for Generative Speech EnhancementCode0
Language and Noise Transfer in Speech Enhancement Generative Adversarial NetworkCode0
Investigating Generative Adversarial Networks based Speech Dereverberation for Robust Speech RecognitionCode0
Investigating the effect of residual and highway connections in speech enhancement modelsCode0
Magnitude-Phase Dual-Path Speech Enhancement Network based on Self-Supervised Embedding and Perceptual Contrast Stretch BoostingCode0
Improved Speech Enhancement with the Wave-U-NetCode0
Improving Design of Input Condition Invariant Speech EnhancementCode0
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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