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 851–900 of 982 papers

TitleStatusHype
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
Speech Modeling with a Hierarchical Transformer Dynamical VAE—0
Speech Quality Assessment Model Based on Mixture of Experts: System-Level Performance Enhancement and Utterance-Level Challenge Analysis—0
SpeechX: Neural Codec Language Model as a Versatile Speech Transformer—0
Spiking Structured State Space Model for Monaural Speech Enhancement—0
Spoken Speech Enhancement using EEG—0
SRIB-LEAP submission to Far-field Multi-Channel Speech Enhancement Challenge for Video Conferencing—0
Stable Training of DNN for Speech Enhancement based on Perceptually-Motivated Black-Box Cost Function—0
Stack Less, Repeat More: A Block Reusing Approach for Progressive Speech Enhancement—0
Statistical Speech Enhancement Based on Probabilistic Integration of Variational Autoencoder and Non-Negative Matrix Factorization—0
Stereo Speech Enhancement Using Custom Mid-Side Signals and Monaural Processing—0
Streaming Noise Context Aware Enhancement For Automatic Speech Recognition in Multi-Talker Environments—0
Student-Teacher Learning for BLSTM Mask-based Speech Enhancement—0
Study of Lightweight Transformer Architectures for Single-Channel Speech Enhancement—0
Sub-Band Knowledge Distillation Framework for Speech Enhancement—0
Subspace Hybrid Beamforming for Head-worn Microphone Arrays—0
Subspace Hybrid MVDR Beamforming for Augmented Hearing—0
SuperM2M: Supervised and Mixture-to-Mixture Co-Learning for Speech Enhancement and Noise-Robust ASR—0
Supervised Speech Separation Based on Deep Learning: An Overview—0
SwinLip: An Efficient Visual Speech Encoder for Lip Reading Using Swin Transformer—0
Switching Variational Auto-Encoders for Noise-Agnostic Audio-visual Speech Enhancement—0
Tackling real noisy reverberant meetings with all-neural source separation, counting, and diarization system—0
Taco-VC: A Single Speaker Tacotron based Voice Conversion with Limited Data—0
TAPS: Throat and Acoustic Paired Speech Dataset for Deep Learning-Based Speech Enhancement—0
Target Speech Extraction with Conditional Diffusion Model—0
Task-Aware Unified Source Separation—0
0/1 Deep Neural Networks via Block Coordinate Descent—0
Task splitting for DNN-based acoustic echo and noise removal—0
TCG CREST System Description for the Second DISPLACE Challenge—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