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

Showing 801850 of 982 papers

TitleStatusHype
Characterizing Speech Adversarial Examples Using Self-Attention U-Net Enhancement0
Tackling real noisy reverberant meetings with all-neural source separation, counting, and diarization system0
Improving noise robust automatic speech recognition with single-channel time-domain enhancement network0
Efficient Trainable Front-Ends for Neural Speech Enhancement0
Consistency-aware multi-channel speech enhancement using deep neural networks0
Boosted Locality Sensitive Hashing: Discriminative Binary Codes for Source SeparationCode0
Speech Enhancement using Self-Adaptation and Multi-Head Self-Attention0
Stable Training of DNN for Speech Enhancement based on Perceptually-Motivated Black-Box Cost Function0
DNN-Based Distributed Multichannel Mask Estimation for Speech Enhancement in Microphone Arrays0
Robust Multi-channel Speech Recognition using Frequency Aligned Network0
Single Channel Speech Enhancement Using Temporal Convolutional Recurrent Neural Networks0
Deep Xi as a Front-End for Robust Automatic Speech Recognition0
CLCNet: Deep learning-based Noise Reduction for Hearing Aids using Complex Linear Coding0
Noise dependent Super Gaussian-Coherence based dual microphone Speech Enhancement for hearing aid application using smartphone0
The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Speech Quality and Testing Framework0
Robust Speaker Recognition Using Speech Enhancement And Attention Model0
Speech Enhancement based on Denoising Autoencoder with Multi-branched EncodersCode0
Monaural Speech Enhancement Using a Multi-Branch Temporal Convolutional Network0
Mixture of Inference Networks for VAE-based Audio-visual Speech Enhancement0
High-quality Speech Synthesis Using Super-resolution Mel-Spectrogram0
Time-Domain Multi-modal Bone/air Conducted Speech Enhancement0
MMTM: Multimodal Transfer Module for CNN FusionCode0
Distributed Microphone Speech Enhancement based on Deep Learning0
Sequential Multi-Frame Neural Beamforming for Speech Separation and Enhancement0
Speaker independence of neural vocoders and their effect on parametric resynthesis speech enhancement0
Robust Unsupervised Audio-visual Speech Enhancement Using a Mixture of Variational Autoencoders0
The Speed Submission to DIHARD II: Contributions & Lessons Learned0
What does a network layer hear? Analyzing hidden representations of end-to-end ASR through speech synthesisCode0
Memory Requirement Reduction of Deep Neural Networks Using Low-bit Quantization of Parameters0
Does Speech enhancement of publicly available data help build robust Speech Recognition Systems?0
Feature Enhancement with Deep Feature Losses for Speaker VerificationCode0
A Recurrent Variational Autoencoder for Speech Enhancement0
Word-level Embeddings for Cross-Task Transfer Learning in Speech ProcessingCode0
Comparative Study between Adversarial Networks and Classical Techniques for Speech Enhancement0
AeGAN: Time-Frequency Speech Denoising via Generative Adversarial Networks0
Multi-Talker MVDR Beamforming Based on Extended Complex Gaussian Mixture Model0
使用語者轉換技術於語音合成資料庫之音質改進(Speech Enhancement for TTS Speech Corpora by using Voice Conversion Technologies)0
Speech enhancement based on the integration of fully convolutional network, temporal lowpass filtering and spectrogram masking0
AV Speech Enhancement Challenge using a Real Noisy Corpus0
Multichannel Speech Enhancement by Raw Waveform-mapping using Fully Convolutional Networks0
An Investigation into the Effectiveness of Enhancement in ASR Training and Test for CHiME-5 Dinner Party TranscriptionCode0
CochleaNet: A Robust Language-independent Audio-Visual Model for Speech Enhancement0
A scalable noisy speech dataset and online subjective test framework0
Spoken Speech Enhancement using EEG0
Generative Speech Enhancement Based on Cloned Networks0
On Loss Functions for Supervised Monaural Time-Domain Speech Enhancement0
Speech Enhancement using Adaptive Mean Median Deviation and EMD Technique0
Coarse-to-fine Optimization for Speech Enhancement0
A Dual-Staged Context Aggregation Method Towards Efficient End-To-End Speech Enhancement0
Audio-visual Speech Enhancement Using Conditional Variational Auto-Encoders0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ROSE-CD(PESQ)PESQ (wb)3.99Unverified
2PESQetarianPESQ (wb)3.82Unverified
3Mamba-SEUNet L (+PCS)PESQ (wb)3.73Unverified
4Schrödinger bridge (PESQ loss)PESQ (wb)3.7Unverified
5SEMamba (+PCS)PESQ (wb)3.69Unverified
6ZipEnhancer (S, \lamba_6 = 0)PESQ (wb)3.63Unverified
7PrimeK-NetPESQ (wb)3.61Unverified
8ZipEnhancer (S, \lamba_6 = 0.2)PESQ (wb)3.61Unverified
9MP-SENetPESQ (wb)3.6Unverified
10PCS_CS_WAVLMPESQ (wb)3.54Unverified
#ModelMetricClaimedVerifiedStatus
1BSRNN-S + MGDSI-SDR-WB21.4Unverified
2DTLNSI-SDR-WB16.34Unverified
3Non-Real-Time MultiScale+SI-SDR-WB16.22Unverified
4ZipEnhancer (M)PESQ-WB3.81Unverified
5TF-Locoformer (M)PESQ-WB3.72Unverified
6ZipEnhancer (S)PESQ-WB3.69Unverified
7MambAttentionPESQ-WB3.67Unverified
8MP-SENetPESQ-WB3.62Unverified
9xLSTM-SENetPESQ-WB3.59Unverified
10BSRNN-S + MRSDPESQ-WB3.53Unverified
#ModelMetricClaimedVerifiedStatus
1Inter-Channel Conv-TasNetSDR19.67Unverified
2CA Dense U-Net (Complex)SDR18.64Unverified
3Dense U-Net (Complex)SDR18.4Unverified
4Dense U-Net (Real)SDR16.86Unverified
5U-Net (Real)SDR15.97Unverified
6Noisy/unprocessedSDR6.5Unverified
#ModelMetricClaimedVerifiedStatus
1Schrödinger Bridge (PESQ loss)PESQ-WB3.09Unverified
2SGMSE+PESQ-WB2.5Unverified
3Demucs v4PESQ-WB2.37Unverified
4Schrödinger BridgePESQ-WB2.33Unverified
5Conv-TasNetPESQ-WB2.31Unverified
6CDiffuSEPESQ-WB1.6Unverified
#ModelMetricClaimedVerifiedStatus
1ReVISE (ch2)Audio Quality MOS4.19Unverified
2ReVISE (bf)Audio Quality MOS4.11Unverified
3Demucs (ch2)Audio Quality MOS2.95Unverified
4Demucs (bf)Audio Quality MOS2.39Unverified
5MaxDI (Baseline)PESQ1.17Unverified
6DAJA (MVDR,HMA,1000) (Overlapped Speech)SDR-4.76Unverified
#ModelMetricClaimedVerifiedStatus
1ZipEnhancer (M)PESQ-NB4.08Unverified
2DCCRN-MCPESQ-NB3.21Unverified
3DCCRN-MPESQ-NB3.15Unverified
4DCCRNPESQ-NB3.04Unverified
5RNN-ModulationPESQ-WB2.75Unverified
#ModelMetricClaimedVerifiedStatus
1MambAttentionESTOI0.8Unverified
2SEMambaESTOI0.8Unverified
3xLSTM-SENetESTOI0.8Unverified
4MP-SENetESTOI0.79Unverified
#ModelMetricClaimedVerifiedStatus
1SepFormerPESQ2.84Unverified
2DTLNPESQ2.23Unverified
3UnprocessedPESQ1.83Unverified
4Non-Real-Time MultiScale+PESQ1.52Unverified
#ModelMetricClaimedVerifiedStatus
1DCUNet-MCPESQ-NB3.44Unverified
2DCCRN-MPESQ-NB3.28Unverified
3DCUNetPESQ-NB3.25Unverified
#ModelMetricClaimedVerifiedStatus
1CleanMel-L-mapDNSMOS3.82Unverified
2SpatialNetDNSMOS BAK3.43Unverified
#ModelMetricClaimedVerifiedStatus
1rose_cd(PESQ )PESQ3.99Unverified
2ROSE-CDPESQ3.49Unverified
#ModelMetricClaimedVerifiedStatus
1Wave-U-NetCBAK3.24Unverified
#ModelMetricClaimedVerifiedStatus
1Audio-Visual concat-refPESQ2.7Unverified
#ModelMetricClaimedVerifiedStatus
1SE-MelGANAudio Quality MOS3.1Unverified
#ModelMetricClaimedVerifiedStatus
1DeFT-ANPESQ3.01Unverified
#ModelMetricClaimedVerifiedStatus
1Audio-Visual concat-refPESQ3.03Unverified
#ModelMetricClaimedVerifiedStatus
1SepFormerPESQ3.07Unverified