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

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Showing 401–450 of 982 papers

TitleStatusHype
McNet: Fuse Multiple Cues for Multichannel Speech EnhancementCode1
Array Configuration-Agnostic Personalized Speech Enhancement using Long-Short-Term Spatial Coherence—0
Leveraging Heteroscedastic Uncertainty in Learning Complex Spectral Mapping for Single-channel Speech Enhancement—0
Hybrid Transformers for Music Source SeparationCode5
Multi-Label Training for Text-Independent Speaker Identification—0
The Potential of Neural Speech Synthesis-based Data Augmentation for Personalized Speech Enhancement—0
SceneFake: An Initial Dataset and Benchmarks for Scene Fake Audio DetectionCode1
Cross-Attention is all you need: Real-Time Streaming Transformers for Personalised Speech Enhancement—0
DiffPhase: Generative Diffusion-based STFT Phase Retrieval—0
Egocentric Audio-Visual Noise Suppression—0
Breaking the trade-off in personalized speech enhancement with cross-task knowledge distillation—0
Self-Supervised Learning for Speech Enhancement through SynthesisCode0
Analysing Diffusion-based Generative Approaches versus Discriminative Approaches for Speech Restoration—0
Speech enhancement using ego-noise references with a microphone array embedded in an unmanned aerial vehicle—0
Real-Time Joint Personalized Speech Enhancement and Acoustic Echo Cancellation—0
Cold Diffusion for Speech Enhancement—0
Iterative autoregression: a novel trick to improve your low-latency speech enhancement model—0
Dynamic Kernels and Channel Attention for Low Resource Speaker Verification—0
Fast and efficient speech enhancement with variational autoencoders—0
A weighted-variance variational autoencoder model for speech enhancement—0
Analysis of Noisy-target Training for DNN-based speech enhancement—0
Inference and Denoise: Causal Inference-based Neural Speech EnhancementCode1
Audio-visual speech enhancement with a deep Kalman filter generative model—0
Exploiting the compressed spectral loss for the learning of the DEMUCS speech enhancement network—0
A Preliminary Study of the Application of Discrete Wavelet Transform Features in Conv-TasNet Speech Enhancement Model—0
SCA: Streaming Cross-attention Alignment for Echo Cancellation—0
Audio-Visual Speech Enhancement and Separation by Utilizing Multi-Modal Self-Supervised Embeddings—0
Diffusion-based Generative Speech Source SeparationCode1
Diffiner: A Versatile Diffusion-based Generative Refiner for Speech EnhancementCode1
A Training and Inference Strategy Using Noisy and Enhanced Speech as Target for Speech Enhancement without Clean SpeechCode0
Parallel Gated Neural Network With Attention Mechanism For Speech Enhancement—0
SCP-GAN: Self-Correcting Discriminator Optimization for Training Consistency Preserving Metric GAN on Speech Enhancement Tasks—0
TridentSE: Guiding Speech Enhancement with 32 Global Tokens—0
Time-Domain Speech Enhancement for Robust Automatic Speech Recognition—0
A Novel Frame Structure for Cloud-Based Audio-Visual Speech Enhancement in Multimodal Hearing-aids—0
Improved Normalizing Flow-Based Speech Enhancement using an All-pole Gammatone Filterbank for Conditional Input Representation—0
spatial-dccrn: dccrn equipped with frame-level angle feature and hybrid filtering for multi-channel speech enhancement—0
Accelerating RNN-based Speech Enhancement on a Multi-Core MCU with Mixed FP16-INT8 Post-Training Quantization—0
LeVoice ASR Systems for the ISCSLP 2022 Intelligent Cockpit Speech Recognition Challenge—0
Binaural Speech Enhancement Using STOI-Optimal Masks—0
Speech Enhancement Using Self-Supervised Pre-Trained Model and Vector Quantization—0
Speech Enhancement with Perceptually-motivated Optimization and Dual Transformations—0
MMS-MSG: A Multi-purpose Multi-Speaker Mixture Signal GeneratorCode1
CMGAN: Conformer-Based Metric-GAN for Monaural Speech EnhancementCode2
GIST-AiTeR System for the Diarization Task of the 2022 VoxCeleb Speaker Recognition Challenge—0
A Universally-Deployable ASR Frontend for Joint Acoustic Echo Cancellation, Speech Enhancement, and Voice Separation—0
Multimodal Speech Enhancement Using Burst Propagation—0
Multi-View Attention Transfer for Efficient Speech Enhancement—0
Speech Enhancement and Dereverberation with Diffusion-based Generative Models—0
DNN-Free Low-Latency Adaptive Speech Enhancement Based on Frame-Online Beamforming Powered by Block-Online FastMNMF—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