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 651–700 of 982 papers

TitleStatusHype
Time Alignment using Lip Images for Frame-based Electrolaryngeal Voice Conversion—0
Time-Domain Multi-modal Bone/air Conducted Speech Enhancement—0
Time-Domain Speech Enhancement for Robust Automatic Speech Recognition—0
Time-Variance Aware Real-Time Speech Enhancement—0
To Dereverb Or Not to Dereverb? Perceptual Studies On Real-Time Dereverberation Targets—0
TouchTTS: An Embarrassingly Simple TTS Framework that Everyone Can Touch—0
Towards Advanced Speech Signal Processing: A Statistical Perspective on Convolution-Based Architectures and its Applications—0
Towards efficient models for real-time deep noise suppression—0
Towards Generalized Speech Enhancement with Generative Adversarial Networks—0
Towards Low-distortion Multi-channel Speech Enhancement: The ESPNet-SE Submission to The L3DAS22 Challenge—0
Towards Robust Real-time Audio-Visual Speech Enhancement—0
Towards Robust Speaker Verification with Target Speaker Enhancement—0
Towards speech enhancement using a variational U-Net architecture—0
Towards Sub-millisecond Latency Real-Time Speech Enhancement Models on Hearables—0
Toward Universal Speech Enhancement for Diverse Input Conditions—0
Trainable Adaptive Window Switching for Speech Enhancement—0
Training Speech Enhancement Systems with Noisy Speech Datasets—0
TRAMBA: A Hybrid Transformer and Mamba Architecture for Practical Audio and Bone Conduction Speech Super Resolution and Enhancement on Mobile and Wearable Platforms—0
Transformers in Speech Processing: A Survey—0
Transformers with Competitive Ensembles of Independent Mechanisms—0
Translation-Invariant Shrinkage/Thresholding of Group Sparse Signals—0
TridentSE: Guiding Speech Enhancement with 32 Global Tokens—0
TRNet: Two-level Refinement Network leveraging Speech Enhancement for Noise Robust Speech Emotion Recognition—0
TSTNN: Two-stage Transformer based Neural Network for Speech Enhancement in the Time Domain—0
TS-URGENet: A Three-stage Universal Robust and Generalizable Speech Enhancement Network—0
Convolutional Recurrent Neural Network with Attention for 3D Speech Enhancement—0
Two-Step Knowledge Distillation for Tiny Speech Enhancement—0
Ultra-Lightweight Speech Separation via Group Communication—0
Ultra Low Complexity Deep Learning Based Noise Suppression—0
Ultra-Low Latency Speech Enhancement - A Comprehensive Study—0
Uncertainty Estimation in Deep Speech Enhancement Using Complex Gaussian Mixture Models—0
UNetGAN: A Robust Speech Enhancement Approach in Time Domain for Extremely Low Signal-to-noise Ratio Condition—0
Unified Architecture and Unsupervised Speech Disentanglement for Speaker Embedding-Free Enrollment in Personalized Speech Enhancement—0
Unifying Robustness and Fidelity: A Comprehensive Study of Pretrained Generative Methods for Speech Enhancement in Adverse Conditions—0
Unrestricted Global Phase Bias-Aware Single-channel Speech Enhancement with Conformer-based Metric GAN—0
Unsupervised Face-Masked Speech Enhancement Using Generative Adversarial Networks With Human-in-the-Loop Assessment Metrics—0
Unsupervised Noise adaptation using Data Simulation—0
Unsupervised Sound Separation Using Mixture Invariant Training—0
Unsupervised Speech Enhancement Based on Multichannel NMF-Informed Beamforming for Noise-Robust Automatic Speech Recognition—0
Unsupervised speech enhancement with deep dynamical generative speech and noise models—0
Unsupervised Speech Enhancement with speech recognition embedding and disentanglement losses—0
UP-Cycle-SENet: Unpaired Phase-aware Speech Enhancement Using Deep Complex Cycle Adversarial Networks—0
URGENT Challenge: Universality, Robustness, and Generalizability For Speech Enhancement—0
uSee: Unified Speech Enhancement and Editing with Conditional Diffusion Models—0
Using recurrences in time and frequency within U-net architecture for speech enhancement—0
Using RLHF to align speech enhancement approaches to mean-opinion quality scores—0
Utterance-Wise Meeting Transcription System Using Asynchronous Distributed Microphones—0
Variational Autoencoder for Personalized Pathological Speech Enhancement—0
Variational Autoencoder for Speech Enhancement with a Noise-Aware Encoder—0
Visual Speech Enhancement—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