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 551–600 of 982 papers

TitleStatusHype
Single channel speech enhancement by colored spectrograms—0
Single Channel Speech Enhancement Using Outlier Detection—0
Single Channel Speech Enhancement Using Temporal Convolutional Recurrent Neural Networks—0
Single-channel speech enhancement using learnable loss mixup—0
Single-Channel Speech Enhancement with Deep Complex U-Networks and Probabilistic Latent Space Models—0
SLMGAN: Exploiting Speech Language Model Representations for Unsupervised Zero-Shot Voice Conversion in GANs—0
SNR-Based Features and Diverse Training Data for Robust DNN-Based Speech Enhancement—0
SNR-Based Teachers-Student Technique for Speech Enhancement—0
SNRi Target Training for Joint Speech Enhancement and Recognition—0
Sparsity-Driven EEG Channel Selection for Brain-Assisted Speech Enhancement—0
spatial-dccrn: dccrn equipped with frame-level angle feature and hybrid filtering for multi-channel speech enhancement—0
Spatial-Filter-Bank-Based Neural Method for Multichannel Speech Enhancement—0
Spatially constrained vs. unconstrained filtering in neural spatiospectral filters for multichannel speech enhancement—0
Speaker independence of neural vocoders and their effect on parametric resynthesis speech enhancement—0
Speaker Recognition Based on Deep Learning: An Overview—0
Speaker Re-identification with Speaker Dependent Speech Enhancement—0
Speaker Reinforcement Using Target Source Extraction for Robust Automatic Speech Recognition—0
Speaking in Wavelet Domain: A Simple and Efficient Approach to Speed up Speech Diffusion Model—0
SpecGrad: Diffusion Probabilistic Model based Neural Vocoder with Adaptive Noise Spectral Shaping—0
Spectral feature mapping with mimic loss for robust speech recognition—0
Spectral Masking with Explicit Time-Context Windowing for Neural Network-Based Monaural Speech Enhancement—0
Speech Bandwidth Expansion Via High Fidelity Generative Adversarial Networks—0
Speech Boosting: Low-Latency Live Speech Enhancement for TWS Earbuds—0
SpeechComposer: Unifying Multiple Speech Tasks with Prompt Composition—0
Speech-Declipping Transformer with Complex Spectrogram and Learnerble Temporal Features—0
Speech denoising by parametric resynthesis—0
Speech enhancement aided end-to-end multi-task learning for voice activity detection—0
Speech Enhancement and Dereverberation with Diffusion-based Generative Models—0
Speech Enhancement-assisted Voice Conversion in Noisy Environments—0
Speech Enhancement Based on Cyclegan with Noise-informed Training—0
Speech enhancement based on the integration of fully convolutional network, temporal lowpass filtering and spectrogram masking—0
Speech enhancement deep-learning architecture for efficient edge processing—0
Speech Enhancement for Wake-Up-Word detection in Voice Assistants—0
Improving Speech Enhancement Performance by Leveraging Contextual Broad Phonetic Class Information—0
Speech Enhancement in Adverse Environments Based on Non-stationary Noise-driven Spectral Subtraction and SNR-dependent Phase Compensation—0
Speech Enhancement Modeling Towards Robust Speech Recognition System—0
Speech Enhancement using Adaptive Mean Median Deviation and EMD Technique—0
Speech Enhancement Using Continuous Embeddings of Neural Audio Codec—0
Speech enhancement using ego-noise references with a microphone array embedded in an unmanned aerial vehicle—0
Speech Enhancement Using Multi-Stage Self-Attentive Temporal Convolutional Networks—0
Speech Enhancement Using Pitch Detection Approach For Noisy Environment—0
Speech Enhancement using Self-Adaptation and Multi-Head Self-Attention—0
Speech Enhancement Using Self-Supervised Pre-Trained Model and Vector Quantization—0
Speech Enhancement using Separable Polling Attention and Global Layer Normalization followed with PReLU—0
Speech enhancement with frequency domain auto-regressive modeling—0
Speech enhancement with mixture-of-deep-experts with clean clustering pre-training—0
Speech Enhancement with Multi-granularity Vector Quantization—0
Speech Enhancement with Perceptually-motivated Optimization and Dual Transformations—0
Speech enhancement with variational autoencoders and alpha-stable distributions—0
Speech-MLP: a simple MLP architecture for speech processing—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