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 801–850 of 982 papers

TitleStatusHype
A Two-Stage Hierarchical Deep Filtering Framework for Real-Time Speech Enhancement—0
A two-step backward compatible fullband speech enhancement system—0
A Survey on Audio Diffusion Models: Text To Speech Synthesis and Enhancement in Generative AI—0
Audio Recording Device Identification Based on Deep Learning—0
Audio-visual End-to-end Multi-channel Speech Separation, Dereverberation and Recognition—0
Audio-visual multi-channel speech separation, dereverberation and recognition—0
Audio-Visual Speech Enhancement and Separation by Utilizing Multi-Modal Self-Supervised Embeddings—0
Audio-Visual Speech Enhancement Using Multimodal Deep Convolutional Neural Networks—0
Audio-Visual Speech Enhancement in Noisy Environments via Emotion-Based Contextual Cues—0
Audio-Visual Speech Enhancement Using Multimodal Deep Convolutional Neural Networks—0
Audio-visual Speech Enhancement Using Conditional Variational Auto-Encoders—0
Audio-Visual Speech Enhancement Using Self-supervised Learning to Improve Speech Intelligibility in Cochlear Implant Simulations—0
Audio-Visual Speech Enhancement With Selective Off-Screen Speech Extraction—0
Audio-visual speech enhancement with a deep Kalman filter generative model—0
Audio-Visual Speech Enhancement with Score-Based Generative Models—0
A Unified Deep Learning Framework for Short-Duration Speaker Verification in Adverse Environments—0
A unified multichannel far-field speech recognition system: combining neural beamforming with attention based end-to-end model—0
A Universally-Deployable ASR Frontend for Joint Acoustic Echo Cancellation, Speech Enhancement, and Voice Separation—0
AutoPrep: An Automatic Preprocessing Framework for In-the-Wild Speech Data—0
Autoregressive Speech Enhancement via Acoustic Tokens—0
AV2Wav: Diffusion-Based Re-synthesis from Continuous Self-supervised Features for Audio-Visual Speech Enhancement—0
AV Speech Enhancement Challenge using a Real Noisy Corpus—0
Batch-normalized joint training for DNN-based distant speech recognition—0
BERT for Joint Multichannel Speech Dereverberation with Spatial-aware Tasks—0
Binaural Speech Enhancement Using STOI-Optimal Masks—0
Blind Acoustic Room Parameter Estimation Using Phase Features—0
Blind Mask to Improve Intelligibility of Non-Stationary Noisy Speech—0
Boosting Noise Robustness of Acoustic Model via Deep Adversarial Training—0
Boosting Objective Scores of a Speech Enhancement Model by MetricGAN Post-processing—0
Breaking the trade-off in personalized speech enhancement with cross-task knowledge distillation—0
Bridging the Gap Between Monaural Speech Enhancement and Recognition with Distortion-Independent Acoustic Modeling—0
Bridging The Multi-Modality Gaps of Audio, Visual and Linguistic for Speech Enhancement—0
Building a Luganda Text-to-Speech Model From Crowdsourced Data—0
Building state-of-the-art distant speech recognition using the CHiME-4 challenge with a setup of speech enhancement baseline—0
Canonical Cortical Graph Neural Networks and its Application for Speech Enhancement in Audio-Visual Hearing Aids—0
Can we steal your vocal identity from the Internet?: Initial investigation of cloning Obama's voice using GAN, WaveNet and low-quality found data—0
Can We Trust Deep Speech Prior?—0
Causal Signal-Based DCCRN with Overlapped-Frame Prediction for Online Speech Enhancement—0
Causal Speech Enhancement with Predicting Semantics based on Quantized Self-supervised Learning Features—0
Cellular Network Speech Enhancement: Removing Background and Transmission Noise—0
Challenges and Opportunities in Multi-device Speech Processing—0
Characterizing Speech Adversarial Examples Using Self-Attention U-Net Enhancement—0
CheapNET: Improving Light-weight speech enhancement network by projected loss function—0
CHiME-6 Challenge:Tackling Multispeaker Speech Recognition for Unsegmented Recordings—0
CLCNet: Deep learning-based Noise Reduction for Hearing Aids using Complex Linear Coding—0
CleanUNet 2: A Hybrid Speech Denoising Model on Waveform and Spectrogram—0
Closing the Gap Between Time-Domain Multi-Channel Speech Enhancement on Real and Simulation Conditions—0
Coarse-to-fine Optimization for Speech Enhancement—0
Cold Diffusion for Speech Enhancement—0
Collaborative Deep Learning for Speech Enhancement: A Run-Time Model Selection Method Using Autoencoders—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