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 401–450 of 982 papers

TitleStatusHype
Coarse-to-fine Optimization for Speech Enhancement—0
A scalable noisy speech dataset and online subjective test framework—0
Guided Speech Enhancement Network—0
Guided Variational Autoencoder for Speech Enhancement With a Supervised Classifier—0
Harmonic and non-Harmonic Based Noisy Reverberant Speech Enhancement in Time Domain—0
Harmonic enhancement using learnable comb filter for light-weight full-band speech enhancement model—0
Flexible Multichannel Speech Enhancement for Noise-Robust Frontend—0
Helsinki Speech Challenge 2024—0
FINALLY: fast and universal speech enhancement with studio-like quality—0
FFC-SE: Fast Fourier Convolution for Speech Enhancement—0
Hidden-Markov-Model Based Speech Enhancement—0
Feature Normalization for Fine-tuning Self-Supervised Models in Speech Enhancement—0
Closing the Gap Between Time-Domain Multi-Channel Speech Enhancement on Real and Simulation Conditions—0
Artifact-free Sound Quality in DNN-based Closed-loop Systems for Audio Processing—0
FB-MSTCN: A Full-Band Single-Channel Speech Enhancement Method Based on Multi-Scale Temporal Convolutional Network—0
Fast Real-time Personalized Speech Enhancement: End-to-End Enhancement Network (E3Net) and Knowledge Distillation—0
High-quality Speech Synthesis Using Super-resolution Mel-Spectrogram—0
CleanUNet 2: A Hybrid Speech Denoising Model on Waveform and Spectrogram—0
Array Geometry-Robust Attention-Based Neural Beamformer for Moving Speakers—0
How Bad Are Artifacts?: Analyzing the Impact of Speech Enhancement Errors on ASR—0
How does end-to-end speech recognition training impact speech enhancement artifacts?—0
How much to Dereverberate? Low-Latency Single-Channel Speech Enhancement in Distant Microphone Scenarios—0
A Meeting Transcription System for an Ad-Hoc Acoustic Sensor Network—0
Human Listening and Live Captioning: Multi-Task Training for Speech Enhancement—0
Fast and efficient speech enhancement with variational autoencoders—0
CLCNet: Deep learning-based Noise Reduction for Hearing Aids using Complex Linear Coding—0
Far-Field Speaker Recognition Benchmark Derived From The DiPCo Corpus—0
Array Configuration-Agnostic Personal Voice Activity Detection Based on Spatial Coherence—0
FADI-AEC: Fast Score Based Diffusion Model Guided by Far-end Signal for Acoustic Echo Cancellation—0
Improved Normalizing Flow-Based Speech Enhancement using an All-pole Gammatone Filterbank for Conditional Input Representation—0
Face Recognition with Machine Learning in OpenCV_ Fusion of the results with the Localization Data of an Acoustic Camera for Speaker Identification—0
Expression-preserving face frontalization improves visually assisted speech processing—0
Improving Character Error Rate Is Not Equal to Having Clean Speech: Speech Enhancement for ASR Systems with Black-box Acoustic Models—0
Improving Design of Input Condition Invariant Speech Enhancement—0
Improving Dual-Microphone Speech Enhancement by Learning Cross-Channel Features with Multi-Head Attention—0
CHiME-6 Challenge:Tackling Multispeaker Speech Recognition for Unsegmented Recordings—0
Improving noise robust automatic speech recognition with single-channel time-domain enhancement network—0
Improving Noise Robustness of Contrastive Speech Representation Learning with Speech Reconstruction—0
Improving Noise Robustness of LLM-based Zero-shot TTS via Discrete Acoustic Token Denoising—0
Array Configuration-Agnostic Personalized Speech Enhancement using Long-Short-Term Spatial Coherence—0
Improving Perceptual Quality, Intelligibility, and Acoustics on VoIP Platforms—0
Improving spatial cues for hearables using a parameterized binaural CDR estimator—0
Sequential Multi-Frame Neural Beamforming for Speech Separation and Enhancement—0
Exploring WavLM on Speech Enhancement—0
Improving the Intent Classification accuracy in Noisy Environment—0
Improving Visual Speech Enhancement Network by Learning Audio-visual Affinity with Multi-head Attention—0
Exploring the Potential of Data-Driven Spatial Audio Enhancement Using a Single-Channel Model—0
Incorporating Multi-Target in Multi-Stage Speech Enhancement Model for Better Generalization—0
CheapNET: Improving Light-weight speech enhancement network by projected loss function—0
Exploring the Best Loss Function for DNN-Based Low-latency Speech Enhancement with Temporal Convolutional Networks—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