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 351–400 of 982 papers

TitleStatusHype
Improving Perceptual Quality, Intelligibility, and Acoustics on VoIP Platforms—0
Subspace Hybrid Beamforming for Head-worn Microphone Arrays—0
TEA-PSE 3.0: Tencent-Ethereal-Audio-Lab Personalized Speech Enhancement System For ICASSP 2023 DNS Challenge—0
Localizing Spatial Information in Neural Spatiospectral Filters—0
Guided Speech Enhancement Network—0
Real-Time Audio-Visual End-to-End Speech Enhancement—0
The NPU-Elevoc Personalized Speech Enhancement System for ICASSP2023 DNS Challenge—0
Blind Acoustic Room Parameter Estimation Using Phase Features—0
Adaptive Dereverberation, Noise and Interferer Reduction Using Sparse Weighted Linearly Constrained Minimum Power Beamforming—0
Improving the Intent Classification accuracy in Noisy Environment—0
Speech Modeling with a Hierarchical Transformer Dynamical VAE—0
Miipher: A Robust Speech Restoration Model Integrating Self-Supervised Speech and Text RepresentationsCode1
Real-time Audio Video Enhancement \ a Microphone Array and Headphones—0
Reducing the Prior Mismatch of Stochastic Differential Equations for Diffusion-based Speech EnhancementCode1
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
Metric-oriented Speech Enhancement using Diffusion Probabilistic Model—0
A Framework for Unified Real-time Personalized and Non-Personalized Speech Enhancement—0
Gradient Remedy for Multi-Task Learning in End-to-End Noise-Robust Speech RecognitionCode1
Unifying Speech Enhancement and Separation with Gradient Modulation for End-to-End Noise-Robust Speech SeparationCode1
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
Speech Enhancement with Multi-granularity Vector Quantization—0
PAAPLoss: A Phonetic-Aligned Acoustic Parameter Loss for Speech EnhancementCode1
TAPLoss: A Temporal Acoustic Parameter Loss for Speech EnhancementCode1
Multi-Channel Target Speaker Extraction with Refinement: The WavLab Submission to the Second Clarity Enhancement Challenge—0
Speech Enhancement for Virtual Meetings on Cellular NetworksCode0
On Batching Variable Size Inputs for Training End-to-End Speech Enhancement Systems—0
Cellular Network Speech Enhancement: Removing Background and Transmission Noise—0
Rethinking complex-valued deep neural networks for monaural speech enhancement—0
Perceive and predict: self-supervised speech representation based loss functions for speech enhancement—0
StoRM: A Diffusion-based Stochastic Regeneration Model for Speech Enhancement and DereverberationCode2
ReVISE: Self-Supervised Speech Resynthesis with Visual Input for Universal and Generalized Speech Enhancement—0
Fast FullSubNet: Accelerate Full-band and Sub-band Fusion Model for Single-channel Speech EnhancementCode2
DeFT-AN: Dense Frequency-Time Attentive Network for Multichannel Speech EnhancementCode1
BASPRO: a balanced script producer for speech corpus collection based on the genetic algorithmCode1
Uncertainty Estimation in Deep Speech Enhancement Using Complex Gaussian Mixture Models—0
SpeechLMScore: Evaluating speech generation using speech language modelCode1
Selector-Enhancer: Learning Dynamic Selection of Local and Non-local Attention Operation for Speech Enhancement—0
Injecting Spatial Information for Monaural Speech Enhancement via Knowledge Distillation—0
Deep neural network techniques for monaural speech enhancement: state of the art analysis—0
High Fidelity Speech Enhancement with Band-split RNNCode1
Stereo Speech Enhancement Using Custom Mid-Side Signals and Monaural Processing—0
Dynamic Acoustic Compensation and Adaptive Focal Training for Personalized Speech Enhancement—0
D²Net: A Denoising and Dereverberation Network Based on Two-branch Encoder and Dual-path Transformer—0
LA-VocE: Low-SNR Audio-visual Speech Enhancement using Neural Vocoders—0
Exploring WavLM on Speech Enhancement—0
A Two-Stage Deep Representation Learning-Based Speech Enhancement Method Using Variational Autoencoder and Adversarial Training—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