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 251–300 of 982 papers

TitleStatusHype
Real-time Speech Enhancement and Separation with a Unified Deep Neural Network for Single/Dual Talker Scenarios—0
A Single Speech Enhancement Model Unifying Dereverberation, Denoising, Speaker Counting, Separation, and Extraction—0
Magnitude-and-phase-aware Speech Enhancement with Parallel Sequence Modeling—0
Psychoacoustic Challenges Of Speech Enhancement On VoIP Platforms—0
VSANet: Real-time Speech Enhancement Based on Voice Activity Detection and Causal Spatial Attention—0
An experiment on an automated literature survey of data-driven speech enhancement methods—0
An Exploration of Task-decoupling on Two-stage Neural Post Filter for Real-time Personalized Acoustic Echo Cancellation—0
LauraGPT: Listen, Attend, Understand, and Regenerate Audio with GPTCode2
MBTFNet: Multi-Band Temporal-Frequency Neural Network For Singing Voice Enhancement—0
A Fused Deep Denoising Sound Coding Strategy for Bilateral Cochlear Implants—0
uSee: Unified Speech Enhancement and Editing with Conditional Diffusion Models—0
Toward Universal Speech Enhancement for Diverse Input Conditions—0
Multichannel Voice Trigger Detection Based on Transform-average-concatenate—0
Does Single-channel Speech Enhancement Improve Keyword Spotting Accuracy? A Case Study—0
AutoPrep: An Automatic Preprocessing Framework for In-the-Wild Speech Data—0
DDTSE: Discriminative Diffusion Model for Target Speech Extraction—0
Speech enhancement with frequency domain auto-regressive modeling—0
ICASSP 2023 Acoustic Echo Cancellation ChallengeCode2
A Multiscale Autoencoder (MSAE) Framework for End-to-End Neural Network Speech Enhancement—0
Deep Complex U-Net with Conformer for Audio-Visual Speech Enhancement—0
Joint Minimum Processing Beamforming and Near-end Listening Enhancement—0
Exploring Speech Enhancement for Low-resource Speech Synthesis—0
Incorporating Ultrasound Tongue Images for Audio-Visual Speech Enhancement—0
Posterior sampling algorithms for unsupervised speech enhancement with recurrent variational autoencoder—0
Unsupervised speech enhancement with diffusion-based generative modelsCode1
Diffusion-based speech enhancement with a weighted generative-supervised learning loss—0
Refining DNN-based Mask Estimation using CGMM-based EM Algorithm for Multi-channel Noise Reduction—0
Single and Few-step Diffusion for Generative Speech EnhancementCode1
Continuous Modeling of the Denoising Process for Speech Enhancement Based on Deep Learning—0
Unifying Robustness and Fidelity: A Comprehensive Study of Pretrained Generative Methods for Speech Enhancement in Adverse Conditions—0
Two-Step Knowledge Distillation for Tiny Speech Enhancement—0
Diff-SV: A Unified Hierarchical Framework for Noise-Robust Speaker Verification Using Score-Based Diffusion Probabilistic ModelsCode1
Multi-dimensional Speech Quality Assessment in CrowdsourcingCode1
AV2Wav: Diffusion-Based Re-synthesis from Continuous Self-supervised Features for Audio-Visual Speech Enhancement—0
Assessing the Generalization Gap of Learning-Based Speech Enhancement Systems in Noisy and Reverberant Environments—0
CleanUNet 2: A Hybrid Speech Denoising Model on Waveform and Spectrogram—0
PlumberNet: Fixing interference leakage after GEV beamformingCode0
Causal Signal-Based DCCRN with Overlapped-Frame Prediction for Online Speech Enhancement—0
Spiking Structured State Space Model for Monaural Speech Enhancement—0
Simulating room transfer functions between transducers mounted on audio devices using a modified image source methodCode1
Single-Channel Speech Enhancement with Deep Complex U-Networks and Probabilistic Latent Space Models—0
Noise robust speech emotion recognition with signal-to-noise ratio adapting speech enhancement—0
DeFTAN-II: Efficient Multichannel Speech Enhancement with Subgroup ProcessingCode1
Rep2wav: Noise Robust text-to-speech Using self-supervised representations—0
Exploiting Time-Frequency Conformers for Music Audio Enhancement—0
AdVerb: Visually Guided Audio Dereverberation—0
Convoifilter: A case study of doing cocktail party speech recognition—0
Explicit Estimation of Magnitude and Phase Spectra in Parallel for High-Quality Speech EnhancementCode2
SpeechX: Neural Codec Language Model as a Versatile Speech Transformer—0
Separate Anything You DescribeCode3
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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