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 451–500 of 982 papers

TitleStatusHype
SLMGAN: Exploiting Speech Language Model Representations for Unsupervised Zero-Shot Voice Conversion in GANs—0
Low bit rate binaural link for improved ultra low-latency low-complexity multichannel speech enhancement in Hearing Aids—0
Audio-Visual Speech Enhancement Using Self-supervised Learning to Improve Speech Intelligibility in Cochlear Implant Simulations—0
Audio-visual End-to-end Multi-channel Speech Separation, Dereverberation and Recognition—0
Multi-Loss Convolutional Network with Time-Frequency Attention for Speech Enhancement—0
Feature Normalization for Fine-tuning Self-Supervised Models in Speech Enhancement—0
Unsupervised speech enhancement with deep dynamical generative speech and noise models—0
Audio-Visual Speech Enhancement With Selective Off-Screen Speech Extraction—0
Efficient Encoder-Decoder and Dual-Path Conformer for Comprehensive Feature Learning in Speech Enhancement—0
Convolutional Recurrent Neural Network with Attention for 3D Speech Enhancement—0
EffCRN: An Efficient Convolutional Recurrent Network for High-Performance Speech Enhancement—0
On the Behavior of Intrusive and Non-intrusive Speech Enhancement Metrics in Predictive and Generative Settings—0
Influence of Lossy Speech Codecs on Hearing-aid, Binaural Sound Source Localisation using DNNs—0
On Crowdsourcing-design with Comparison Category Rating for Evaluating Speech Enhancement Algorithms—0
Audio-Visual Speech Enhancement with Score-Based Generative Models—0
Harmonic enhancement using learnable comb filter for light-weight full-band speech enhancement model—0
Downstream Task Agnostic Speech Enhancement with Self-Supervised Representation Loss—0
Incorporating Ultrasound Tongue Images for Audio-Visual Speech Enhancement through Knowledge Distillation—0
SE-Bridge: Speech Enhancement with Consistent Brownian Bridge—0
DCCRN-KWS: an audio bias based model for noise robust small-footprint keyword spotting—0
Diffusion-Based Speech Enhancement with Joint Generative and Predictive Decoders—0
Diffusion-based Signal Refiner for Speech Separation—0
All Information is Necessary: Integrating Speech Positive and Negative Information by Contrastive Learning for Speech Enhancement—0
Neural Speech Enhancement with Very Low Algorithmic Latency and Complexity via Integrated Full- and Sub-Band Modeling—0
Array Configuration-Agnostic Personal Voice Activity Detection Based on Spatial Coherence—0
The future of hearing aid technology—0
Wav2code: Restore Clean Speech Representations via Codebook Lookup for Noise-Robust ASR—0
A Survey on Audio Diffusion Models: Text To Speech Synthesis and Enhancement in Generative AI—0
Attention-based Speech Enhancement Using Human Quality Perception Modelling—0
Transformers in Speech Processing: A Survey—0
Improving Perceptual Quality, Intelligibility, and Acoustics on VoIP Platforms—0
Subspace Hybrid Beamforming for Head-worn Microphone Arrays—0
Localizing Spatial Information in Neural Spatiospectral Filters—0
TEA-PSE 3.0: Tencent-Ethereal-Audio-Lab Personalized Speech Enhancement System For ICASSP 2023 DNS Challenge—0
Adaptive Dereverberation, Noise and Interferer Reduction Using Sparse Weighted Linearly Constrained Minimum Power Beamforming—0
The NPU-Elevoc Personalized Speech Enhancement System for ICASSP2023 DNS Challenge—0
Real-Time Audio-Visual End-to-End Speech Enhancement—0
Blind Acoustic Room Parameter Estimation Using Phase Features—0
Guided Speech Enhancement Network—0
Improving the Intent Classification accuracy in Noisy Environment—0
Speech Modeling with a Hierarchical Transformer Dynamical VAE—0
Real-time Audio Video Enhancement \ a Microphone Array and Headphones—0
DFSNet: A Steerable Neural Beamformer Invariant to Microphone Array Configuration for Real-Time, Low-Latency Speech Enhancement—0
Time-Variance Aware Real-Time Speech Enhancement—0
Unsupervised Noise adaptation using Data Simulation—0
A Framework for Unified Real-time Personalized and Non-Personalized Speech Enhancement—0
Metric-oriented Speech Enhancement using Diffusion Probabilistic Model—0
Real-time speech enhancement with dynamic attention span—0
Real-Time Speech Enhancement Using Spectral Subtraction with Minimum Statistics and Spectral Floor—0
Personalized speech enhancement combining band-split RNN and speaker attentive module—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