Mumolo, E.[Enzo],
Spectral domain texture analysis for speech enhancement,
PR(35), No. 10, October 2002, pp. 2181-2191.
Elsevier DOI
0206
BibRef
Chang, J.H.[Joon-Hyuk],
Gazor, S.[Saeed],
Kim, N.S.[Nam Soo],
Mitra, S.K.[Sanjit K.],
Multiple statistical models for soft decision in noisy speech
enhancement,
PR(40), No. 3, March 2007, pp. 1123-1134.
Elsevier DOI
0611
Speech enhancement; DCT; Multiple statistical model; Gaussian;
Laplacian; Gamma; GOF; PSFM; SAP; PESQ
BibRef
Esch, T.,
Rungeler, M.,
Heese, F.,
Vary, P.,
Estimation of Rapidly Time-Varying Harmonic Noise for Speech
Enhancement,
SPLetters(19), No. 10, October 2012, pp. 659-662.
IEEE DOI
1209
BibRef
Mohammadiha, N.,
Martin, R.,
Leijon, A.,
Spectral Domain Speech Enhancement Using HMM State-Dependent
Super-Gaussian Priors,
SPLetters(20), No. 3, March 2013, pp. 253-256.
IEEE DOI
1303
BibRef
Taal, C.H.,
Jensen, J.,
Leijon, A.,
On Optimal Linear Filtering of Speech for Near-End Listening
Enhancement,
SPLetters(20), No. 3, March 2013, pp. 225-228.
IEEE DOI
1303
BibRef
Szurley, J.,
Bertrand, A.,
Moonen, M.,
On the Use of Time-Domain Widely Linear Filtering
for Binaural Speech Enhancement,
SPLetters(20), No. 7, 2013, pp. 649-652.
IEEE DOI
1307
speech enhancement
BibRef
Mowlaee, P.,
Saeidi, R.,
Iterative Closed-Loop Phase-Aware Single-Channel Speech Enhancement,
SPLetters(20), No. 12, 2013, pp. 1235-1239.
IEEE DOI
1311
Delays
BibRef
Kulmer, J.,
Mowlaee, P.,
Phase Estimation in Single Channel Speech Enhancement Using Phase
Decomposition,
SPLetters(22), No. 5, May 2015, pp. 598-602.
IEEE DOI
1411
Harmonic analysis
BibRef
Xu, Y.[Yong],
Du, J.[Jun],
Dai, L.R.[Li-Rong],
Lee, C.H.[Chin-Hui],
An Experimental Study on Speech Enhancement Based on Deep Neural
Networks,
SPLetters(21), No. 1, January 2014, pp. 65-68.
IEEE DOI
1402
BibRef
Jin, Y.G.[Yu Gwang],
Shin, J.W.[Jong Won],
Kim, N.S.[Nam Soo],
Spectro-Temporal Filtering for Multichannel Speech Enhancement in
Short-Time Fourier Transform Domain,
SPLetters(21), No. 3, March 2014, pp. 352-355.
IEEE DOI
1403
Fourier transforms
BibRef
Kwon, K.[Kisoo],
Shin, J.W.[Jong Won],
Kim, N.S.[Nam Soo],
NMF-Based Speech Enhancement Using Bases Update,
SPLetters(22), No. 4, April 2015, pp. 450-454.
IEEE DOI
1411
matrix decomposition
BibRef
Kim, M.,
Smaragdis, P.,
Mixtures of Local Dictionaries for Unsupervised Speech Enhancement,
SPLetters(22), No. 3, March 2015, pp. 293-297.
IEEE DOI
1410
Dictionaries
BibRef
Kleijn, W.B.,
Hendriks, R.C.,
A Simple Model of Speech Communication and its Application to
Intelligibility Enhancement,
SPLetters(22), No. 3, March 2015, pp. 303-307.
IEEE DOI
1410
Auditory system
BibRef
Doclo, S.,
Kellermann, W.,
Makino, S.,
Nordholm, S.E.,
Multichannel Signal Enhancement Algorithms for Assisted Listening
Devices: Exploiting spatial diversity using multiple microphones,
SPMag(32), No. 2, March 2015, pp. 18-30.
IEEE DOI
1503
audio signal processing
BibRef
Kleijn, W.B.,
Crespo, J.B.,
Hendriks, R.C.,
Petkov, P.,
Sauert, B.,
Vary, P.,
Optimizing Speech Intelligibility in a Noisy Environment:
A unified view,
SPMag(32), No. 2, March 2015, pp. 43-54.
IEEE DOI
1503
speech enhancement
BibRef
Gerkmann, T.,
Krawczyk-Becker, M.,
Le Roux, J.,
Phase Processing for Single-Channel Speech Enhancement:
History and recent advances,
SPMag(32), No. 2, March 2015, pp. 55-66.
IEEE DOI
1503
array signal processing
BibRef
Tong, R.J.[Ren-Jie],
Bao, G.Z.[Guang-Zhao],
Ye, Z.F.[Zhong-Fu],
A Higher Order Subspace Algorithm for Multichannel Speech Enhancement,
SPLetters(22), No. 11, November 2015, pp. 2004-2008.
IEEE DOI
1509
AWGN
BibRef
Tavares, R.,
Coelho, R.,
Speech Enhancement with Nonstationary Acoustic Noise Detection in
Time Domain,
SPLetters(23), No. 1, January 2016, pp. 6-10.
IEEE DOI
1601
speech enhancement
BibRef
Gholami-Boroujeny, S.[Shiva],
Fallatah, A.[Anwar],
Heffernan, B.P.[Brian P.],
Dajani, H.R.[Hilmi R.],
Neural network-based adaptive noise cancellation for enhancement of
speech auditory brainstem responses,
SIViP(10), No. 1, February 2016, pp. 389-395.
Springer DOI
1601
BibRef
Luo, Y.[You],
Bao, G.Z.[Guang-Zhao],
Xu, Y.F.[Yang-Fei],
Ye, Z.F.[Zhong-Fu],
Supervised Monaural Speech Enhancement Using Complementary Joint
Sparse Representations,
SPLetters(23), No. 2, February 2016, pp. 237-241.
IEEE DOI
1602
BibRef
Chung, H.,
Plourde, E.,
Champagne, B.,
Discriminative Training of NMF Model Based on Class Probabilities for
Speech Enhancement,
SPLetters(23), No. 4, April 2016, pp. 502-506.
IEEE DOI
1604
Convergence
BibRef
Wang, S.S.,
Chern, A.,
Tsao, Y.,
Hung, J.W.,
Lu, X.,
Lai, Y.H.,
Su, B.,
Wavelet Speech Enhancement Based on Nonnegative Matrix Factorization,
SPLetters(23), No. 8, August 2016, pp. 1101-1105.
IEEE DOI
1608
Fourier transforms
BibRef
Petkov, P.N.,
Stylianou, Y.,
Adaptive Gain Control for Enhanced Speech Intelligibility Under
Reverberation,
SPLetters(23), No. 10, October 2016, pp. 1434-1438.
IEEE DOI
1610
adaptive control
BibRef
Sun, P.,
Qin, J.,
Low-Rank and Sparsity Analysis Applied to Speech Enhancement Via
Online Estimated Dictionary,
SPLetters(23), No. 12, December 2016, pp. 1862-1866.
IEEE DOI
1612
expectation-maximisation algorithm
BibRef
Reddy, C.K.A.[C. Karadagur Ananda],
Shankar, N.,
Bhat, G.S.[G. Shreedhar],
Charan, R.,
Panahi, I.,
An Individualized Super-Gaussian Single Microphone Speech Enhancement
for Hearing Aid Users With Smartphone as an Assistive Device,
SPLetters(24), No. 11, November 2017, pp. 1601-1605.
IEEE DOI
1710
hearing aids, maximum likelihood estimation, signal denoising
BibRef
van Kuyk, S.,
Kleijn, W.B.,
Hendriks, R.C.,
An Instrumental Intelligibility Metric Based on Information Theory,
SPLetters(25), No. 1, January 2018, pp. 115-119.
IEEE DOI
1801
information theory, speech enhancement, speech intelligibility,
SIIB, information theoretic intelligibility metrics,
mutual information
BibRef
Zão, L.,
Coelho, R.,
On the Estimation of Fundamental Frequency From Nonstationary Noisy
Speech Signals Based on the Hilbert-Huang Transform,
SPLetters(25), No. 2, February 2018, pp. 248-252.
IEEE DOI
1802
Hilbert transforms, speech enhancement, HHT-Amp,
Hilbert-Huang transform, decomposition modes,
nonstationary acoustic noises
BibRef
Lee, J.,
Skoglund, J.,
Shabestary, T.,
Kang, H.,
Phase-Sensitive Joint Learning Algorithms for Deep Learning-Based
Speech Enhancement,
SPLetters(25), No. 8, August 2018, pp. 1276-1280.
IEEE DOI
1808
learning (artificial intelligence), speech enhancement,
time-frequency analysis, single-channel speech enhancement
BibRef
Martín-Doñas, J.M.,
Gomez, A.M.,
Gonzalez, J.A.,
Peinado, A.M.,
A Deep Learning Loss Function Based on the Perceptual Evaluation of
the Speech Quality,
SPLetters(25), No. 11, November 2018, pp. 1680-1684.
IEEE DOI
1811
distortion, learning (artificial intelligence),
mean square error methods, neural nets, speech enhancement,
DNN
BibRef
Kim, G.,
Lee, H.,
Kim, B.,
Oh, S.,
Lee, S.,
Unpaired Speech Enhancement by Acoustic and Adversarial Supervision
for Speech Recognition,
SPLetters(26), No. 1, January 2019, pp. 159-163.
IEEE DOI
1901
learning (artificial intelligence), natural language processing,
neural nets, signal classification, generative adversarial network
BibRef
Lei, P.[Peng],
Chen, M.L.[Mei-Ling],
Wang, J.[Jun],
Speech enhancement for in-vehicle voice control systems using wavelet
analysis and blind source separation,
IET-ITS(13), No. 4, April 2019, pp. 693-702.
DOI Link
1903
BibRef
Ram, R.[Rashmirekha],
Mohanty, M.N.[Mihir Narayan],
Use of radial basis function network with discrete wavelet transform
for speech enhancement,
IJCVR(9), No. 2, 2019, pp. 207-223.
DOI Link
1904
BibRef
Kim, J.,
Hahn, M.,
Speech Enhancement Using a Two-Stage Network for an Efficient
Boosting Strategy,
SPLetters(26), No. 5, May 2019, pp. 770-774.
IEEE DOI
1905
adaptive filters, computational complexity,
learning (artificial intelligence), neural net architecture,
two-stage network
BibRef
Nakatani, T.,
Kinoshita, K.,
A Unified Convolutional Beamformer for Simultaneous Denoising and
Dereverberation,
SPLetters(26), No. 6, June 2019, pp. 903-907.
IEEE DOI
1906
array signal processing, signal denoising, speech enhancement,
speech recognition, simultaneous denoising,
robust speech recognition
BibRef
Li, X.,
Leglaive, S.,
Girin, L.,
Horaud, R.,
Audio-Noise Power Spectral Density Estimation Using Long Short-Term
Memory,
SPLetters(26), No. 6, June 2019, pp. 918-922.
IEEE DOI
1906
audio signal processing, Fourier transforms,
learning (artificial intelligence), speech enhancement,
speech enhancement
BibRef
Fu, S.,
Liao, C.,
Tsao, Y.,
Learning With Learned Loss Function: Speech Enhancement With
Quality-Net to Improve Perceptual Evaluation of Speech Quality,
SPLetters(27), 2020, pp. 26-30.
IEEE DOI
2001
Perception optimization, PESQ, speech enhancement, speech quality assessment
BibRef
Wu, J.,
Yu, C.,
Fu, S.,
Liu, C.,
Chien, S.,
Tsao, Y.,
Increasing Compactness of Deep Learning Based Speech Enhancement
Models With Parameter Pruning and Quantization Techniques,
SPLetters(26), No. 12, December 2019, pp. 1887-1891.
IEEE DOI
2001
distributed processing, learning (artificial intelligence),
neural nets, quantisation (signal), signal denoising,
Low Computational Cost
BibRef
Yu, C.,
Hung, K.,
Wang, S.,
Tsao, Y.,
Hung, J.,
Time-Domain Multi-Modal Bone/Air Conducted Speech Enhancement,
SPLetters(27), 2020, pp. 1035-1039.
IEEE DOI
2007
Noise measurement, Speech enhancement, Task analysis, Training,
Time-domain analysis, Convolution, Multi-modal,
fusion strategy
BibRef
Hsieh, T.A.,
Wang, H.M.,
Lu, X.,
Tsao, Y.,
WaveCRN: An Efficient Convolutional Recurrent Neural Network for
End-to-End Speech Enhancement,
SPLetters(27), 2020, pp. 2149-2153.
IEEE DOI
2012
Speech enhancement, Feature extraction, Task analysis, Noise reduction,
Convolution, Noise measurement, Training, simple recurrent unit
BibRef
Siniscalchi, S.M.,
Vector-to-Vector Regression via Distributional Loss for Speech
Enhancement,
SPLetters(28), 2021, pp. 254-258.
IEEE DOI
2102
Noise measurement, Speech enhancement, Predictive models,
Data models, Training, Histograms, Linear programming,
speech enhancement
BibRef
Cui, Z.[Zihao],
Bao, C.C.[Chang-Chun],
Power Exponent Based Weighting Criterion for DNN-Based Mask
Approximation in Speech Enhancement,
SPLetters(28), 2021, pp. 618-622.
IEEE DOI
2104
Speech enhancement, Noise measurement, Linear programming,
Training, Databases, Indexes, Time-frequency analysis,
speech enhancement
BibRef
Witkowski, M.[Marcin],
Kowalczyk, K.[Konrad],
Split Bregman Approach to Linear Prediction Based Dereverberation
With Enforced Speech Sparsity,
SPLetters(28), 2021, pp. 942-946.
IEEE DOI
2106
Microphones, Reverberation, Cost function, Time-frequency analysis,
Standards, Speech enhancement, Mathematical model, speech sparsity
BibRef
Pan, N.N.[Ning-Ning],
Wang, Y.Z.[Yu-Zhu],
Chen, J.D.[Jing-Dong],
Benesty, J.[Jacob],
A Single-Input/Binaural-Output Antiphasic Speech Enhancement Method
for Speech Intelligibility Improvement,
SPLetters(28), 2021, pp. 1445-1449.
IEEE DOI
2108
Convolution, Rendering (computer graphics), Ear,
Speech enhancement, Training, Noise measurement, Decoding, intelligibility
BibRef
Xiang, X.X.[Xiao-Xiao],
Zhang, X.J.[Xiao-Juan],
Chen, H.Z.[Hao-Zhe],
A Convolutional Network With Multi-Scale and Attention Mechanisms for
End-to-End Single-Channel Speech Enhancement,
SPLetters(28), 2021, pp. 1455-1459.
IEEE DOI
2108
Convolution, Speech enhancement, Noise measurement, Decoding,
Training, Feature extraction, Time-domain analysis,
dense connectivity
BibRef
Xiang, X.X.[Xiao-Xiao],
Zhang, X.J.[Xiao-Juan],
Chen, H.Z.[Hao-Zhe],
Two-Stage Learning and Fusion Network With Noise Aware for
Time-Domain Monaural Speech Enhancement,
SPLetters(28), 2021, pp. 1754-1758.
IEEE DOI
2109
Convolution, Decoding, Logic gates, Speech enhancement, Training,
Noise measurement, Signal to noise ratio, Speech enhancement,
dilated dense block
BibRef
Li, G.[Gang],
Wang, X.C.[Xiao-Chen],
Hu, R.M.[Rui-Min],
Zhang, H.Y.[Hu-Yin],
Ke, S.F.[Shan-Fa],
Intelligibility Enhancement Via Normal-to-Lombard Speech Conversion
With Long Short-Term Memory Network and Bayesian Gaussian Mixture
Model,
MultMed(23), 2021, pp. 3035-3047.
IEEE DOI
2109
Vocoders, Speech enhancement,
Working environment noise, Real-time systems, Delays, speech conversion
BibRef
Cheng, L.B.[Long-Biao],
Li, J.F.[Jun-Feng],
Yan, Y.H.[Yong-Hong],
FSCNet: Feature-Specific Convolution Neural Network for Real-Time
Speech Enhancement,
SPLetters(28), 2021, pp. 1958-1962.
IEEE DOI
2110
Convolution, Speech enhancement, Power capacitors, Kernel,
Feature extraction, Time-frequency analysis, Decoding, speech enhancement
BibRef
Tai, W.X.[Wen-Xin],
Lan, T.[Tian],
Wang, Q.H.[Qian-Hui],
Liu, Q.[Qiao],
IDANet: An Information Distillation and Aggregation Network for
Speech Enhancement,
SPLetters(28), 2021, pp. 1998-2002.
IEEE DOI
2110
Convolution, Feature extraction, Speech enhancement,
Noise measurement, Decoding, Encoding, Training, Speech enhancement,
deformable convolution
BibRef
Wang, Z.Q.[Zhong-Qiu],
Wichern, G.[Gordon],
Le Roux, J.[Jonathan],
On the Compensation Between Magnitude and Phase in Speech Separation,
SPLetters(28), 2021, pp. 2018-2022.
IEEE DOI
2110
Time-domain analysis, Measurement, Training, Speech enhancement,
Spectrogram, Signal to noise ratio, Task analysis, deep learning
BibRef
Xiang, X.X.[Xiao-Xiao],
Zhang, X.J.[Xiao-Juan],
Chen, H.Z.[Hao-Zhe],
A Nested U-Net With Self-Attention and Dense Connectivity for
Monaural Speech Enhancement,
SPLetters(29), 2022, pp. 105-109.
IEEE DOI
2202
Convolution, Speech enhancement, Feature extraction, Decoding,
Time-domain analysis, Signal to noise ratio, Sensors, time-domain
BibRef
Wang, Z.Q.[Zhong-Qiu],
Watanabe, S.[Shinji],
Improving Frame-Online Neural Speech Enhancement With
Overlapped-Frame Prediction,
SPLetters(29), 2022, pp. 1422-1426.
IEEE DOI
2207
Prediction algorithms, Speech enhancement,
Discrete Fourier transforms, Spectrogram, Predictive models,
online speech enhancement
BibRef
Kim, H.[Hansol],
Kang, K.[Kyeongmuk],
Shin, J.W.[Jong Won],
Factorized MVDR Deep Beamforming for Multi-Channel Speech Enhancement,
SPLetters(29), 2022, pp. 1898-1902.
IEEE DOI
2209
Speech enhancement, Estimation, Artificial neural networks,
MISO communication, Array signal processing, Deep learning,
factorized MVDR beamformer
BibRef
Fras, M.[Mieszko],
Kowalczyk, K.[Konrad],
Convolutional Weighted Parametric Multichannel Wiener Filter for
Reverberant Source Separation,
SPLetters(29), 2022, pp. 1928-1932.
IEEE DOI
2209
Wiener filters, Convolution, Reverberation, Microphones, Distortion,
Speech enhancement, Transfer functions, Source separation, Wiener filter
BibRef
Hwang, S.[Soojoong],
Lee, E.[Eunkyun],
Jang, I.[Inseon],
Shin, J.W.[Jong Won],
Alias-and-Separate: Wideband Speech Coding Using Sub-Nyquist Sampling
and Speech Separation,
SPLetters(29), 2022, pp. 2003-2007.
IEEE DOI
2210
Speech coding, Bit rate, Wideband, Encoding, Narrowband, Decoding,
Speech enhancement, Frequency aliasing, speech codec, audio codec,
coded signal enhancement
BibRef
Yadav, S.K.[Shekhar Kumar],
George, N.V.[Nithin V.],
Sparse Distortionless Modal Beamforming for Spherical Microphone
Arrays,
SPLetters(29), 2022, pp. 2068-2072.
IEEE DOI
2211
Array signal processing, Microphone arrays, Harmonic analysis,
Acoustic distortion, Speech enhancement, Power harmonic filters,
sparse priors
BibRef
Lee, J.Y.[Jin-Young],
Kang, H.G.[Hong-Goo],
Two-Stage Refinement of Magnitude and Complex Spectra for Real-Time
Speech Enhancement,
SPLetters(29), 2022, pp. 2188-2192.
IEEE DOI
2212
Convolution, Speech enhancement, Noise measurement, Estimation,
Training, Time-frequency analysis, Kernel, two-stage network
BibRef
Yu, R.X.[Run-Xiang],
Zhao, Z.W.[Zi-Wei],
Ye, Z.F.[Zhong-Fu],
PFRNet: Dual-Branch Progressive Fusion Rectification Network for
Monaural Speech Enhancement,
SPLetters(29), 2022, pp. 2358-2362.
IEEE DOI
2212
Feature extraction, Transformers, Speech enhancement, Tensors,
Convolution, Decoding, Time-frequency analysis,
monaural speech enhancement
BibRef
Rosenbaum, T.[Tomer],
Cohen, I.[Israel],
Winebrand, E.[Emil],
Gabso, O.[Ofri],
Differentiable Mean Opinion Score Regularization for Perceptual
Speech Enhancement,
PRL(166), 2023, pp. 159-163.
Elsevier DOI
2302
Speech enhancement, Mean opinion score,
Speech quality assessment, Speech naturalness assessment
BibRef
Lee, D.[Dongheon],
Choi, J.W.[Jung-Woo],
DeFT-AN: Dense Frequency-Time Attentive Network for Multichannel
Speech Enhancement,
SPLetters(30), 2023, pp. 155-159.
IEEE DOI
2303
Speech enhancement, Transformers, Noise measurement, Convolution,
Time-frequency analysis, Time-domain analysis,
transformer
BibRef
Wang, T.T.[Ting-Ting],
Pan, Z.[Zexu],
Ge, M.[Meng],
Yang, Z.[Zhen],
Li, H.Z.[Hai-Zhou],
Time-Domain Speech Separation Networks With Graph Encoding Auxiliary,
SPLetters(30), 2023, pp. 110-114.
IEEE DOI
2303
Time-domain analysis, Encoding, Speech recognition, Convolution,
Speech enhancement, Signal to noise ratio, Transforms,
graph neural networks
BibRef
Duan, Y.[Yicun],
Ren, J.F.[Jian-Feng],
Yu, H.[Heng],
Jiang, X.D.[Xu-Dong],
GAN-in-GAN for Monaural Speech Enhancement,
SPLetters(30), 2023, pp. 853-857.
IEEE DOI
2307
Spectrogram, Generative adversarial networks, Training, Noise measurement,
Generators, Noise reduction, Decoding, speech enhancement
BibRef
Ai, Y.[Yang],
Lu, Y.X.[Ye-Xin],
Ling, Z.H.[Zhen-Hua],
Long-Frame-Shift Neural Speech Phase Prediction With Spectral
Continuity Enhancement and Interpolation Error Compensation,
SPLetters(30), 2023, pp. 1097-1101.
IEEE DOI
2310
BibRef
Xiong, J.W.[Jun-Wen],
Zhou, Y.[Yu],
Zhang, P.[Peng],
Xie, L.[Lei],
Huang, W.[Wei],
Zha, Y.F.[Yu-Fei],
Look&listen: Multi-Modal Correlation Learning for Active Speaker
Detection and Speech Enhancement,
MultMed(25), 2023, pp. 5800-5812.
IEEE DOI
2311
BibRef
Liang, X.W.[Xing-Wei],
Zhang, L.[Lu],
Wu, Z.Y.[Zhi-Yong],
Xu, R.F.[Rui-Feng],
Lite-RTSE: Exploring a Cost-Effective Lite DNN Model for Real-Time
Speech Enhancement in RTC Scenarios,
SPLetters(30), 2023, pp. 1697-1701.
IEEE DOI
2312
BibRef
Yechuri, S.[Sivaramakrishna],
Vanabathina, S.D.[Sunny Dayal],
Genetic Algorithm-Based Adaptive Wiener Gain for Speech Enhancement
Using an Iterative Posterior NMF,
IJIG(23), No. 6 2023, pp. 2350054.
DOI Link
2312
BibRef
O'Shaughnessy, D.[Douglas],
Speech Enhancement: A Review of Modern Methods,
HMS(54), No. 1, February 2024, pp. 110-120.
IEEE DOI
2402
Survey, Speech Enhancement. Acoustic distortion, Acoustics, Speech enhancement, Speech coding,
Reverberation, Noise measurement, Microphones.
BibRef
Xu, X.[Xinmeng],
Improving Monaural Speech Enhancement by Mapping to Fixed Simulation
Space With Knowledge Distillation,
SPLetters(31), 2024, pp. 386-390.
IEEE DOI
2402
Feature extraction, Speech enhancement, Spectrogram,
Noise measurement, Training, Recording, Convolution,
knowledge distillation
BibRef
Xiang, B.[Bajian],
Mao, W.Y.[Wen-Yu],
Tan, K.J.[Kai-Jun],
Lu, H.X.[Hua-Xiang],
CAT-DUnet: Enhancing Speech Dereverberation via Feature Fusion and
Structural Similarity Loss,
SPLetters(31), 2024, pp. 456-460.
IEEE DOI
2402
Spectrogram, Convolution, Time-frequency analysis, Measurement,
Training, Feature extraction, Deep learning, Attention mechanism,
speech dereverberation
BibRef
Park, H.J.[Hyun Joon],
Shin, W.[Wooseok],
Kim, J.S.[Jin Sob],
Han, S.W.[Sung Won],
Leveraging Non-Causal Knowledge via Cross-Network Knowledge
Distillation for Real-Time Speech Enhancement,
SPLetters(31), 2024, pp. 1129-1133.
IEEE DOI
2405
Real-time systems, Knowledge engineering, Convolution, Cause effect analysis,
Speech enhancement, Decoding, Pipelines, non-causal knowledge
BibRef
Jannu, C.[Chaitanya],
Vanambathina, S.D.[Sunny Dayal],
Shuffle Attention U-Net for Speech Enhancement in Time Domain,
IJIG(24), No. 4, July 2024, pp. 2450043.
DOI Link
2408
BibRef
Han, R.[Runduo],
Xu, W.M.[Wei-Ming],
Zhang, Z.[Zihan],
Liu, M.S.[Ming-Shuai],
Xie, L.[Lei],
Distil-DCCRN: A Small-Footprint DCCRN Leveraging Feature-Based
Knowledge Distillation in Speech Enhancement,
SPLetters(31), 2024, pp. 2075-2079.
IEEE DOI
2408
Feature extraction, Speech enhancement, Decoding,
Time-frequency analysis, Standards, Long short term memory,
knowledge distillation
BibRef
Gonzalez, P.[Philippe],
Tan, Z.H.[Zheng-Hua],
Østergaard, J.[Jan],
Jensen, J.[Jesper],
Alstrøm, T.S.[Tommy Sonne],
May, T.[Tobias],
The Effect of Training Dataset Size on Discriminative and
Diffusion-Based Speech Enhancement Systems,
SPLetters(31), 2024, pp. 2225-2229.
IEEE DOI
2409
Training, Noise, Speech enhancement, Databases, Reflection, Receivers,
Ear, Speech enhancement, training data, discriminative models, diffusion models
BibRef
Quan, C.S.[Chang-Sheng],
Li, X.F.[Xiao-Fei],
Multichannel Long-Term Streaming Neural Speech Enhancement for Static
and Moving Speakers,
SPLetters(31), 2024, pp. 2295-2299.
IEEE DOI
2410
Speech enhancement, Training, Convolution, Extrapolation,
Complexity theory, Vectors, Streams, Streaming, speech denoising,
multi-channel speech enhancement
BibRef
Hao, Y.[Yiya],
Xiong, F.F.[Fei-Fei],
Li, B.[Bei],
Ding, N.[Nai],
Feng, J.[Jinwei],
EMDSQA: A Neural Speech Quality Assessment Model With Speaker
Embedding,
SPLetters(31), 2024, pp. 3064-3068.
IEEE DOI
2411
Quality assessment, Training, Speech enhancement,
Nuclear magnetic resonance, Vectors, Feature extraction, Accuracy,
online communication speech
BibRef
Yang, Z.[Ziye],
Song, X.[Xiang],
Chen, J.[Jie],
Richard, C.[Cédric],
Cohen, I.[Israel],
Learning Noise Adapters for Incremental Speech Enhancement,
SPLetters(31), 2024, pp. 2915-2919.
IEEE DOI
2411
Noise, Training, Adaptation models, Speech enhancement, Decoding,
Data models, Transformers, Speech recognition, speech enhancement
BibRef
Jannu, C.[Chaitanya],
Vanambathina, S.D.[Sunny Dayal],
Self-Attention-Based Convolutional GRU for Enhancement of Adversarial
Speech Examples,
IJIG(24), No. 6, November 2024, pp. 2450053.
DOI Link
2501
BibRef
Guo, Z.[Zilu],
Du, J.[Jun],
Siniscalchi, S.M.[Sabato Marco],
Pan, J.[Jia],
Liu, Q.F.[Qing-Feng],
Controllable Conformer for Speech Enhancement and Recognition,
SPLetters(32), 2025, pp. 156-160.
IEEE DOI
2501
Noise reduction, Speech enhancement, Signal to noise ratio,
Estimation, Speech recognition, Noise measurement, Schedules, ASR
BibRef
Wang, C.Z.[Cheng-Zhong],
Gu, J.J.[Jian-Jun],
Yao, D.D.[Ding-Ding],
Li, J.F.[Jun-Feng],
Yan, Y.H.[Yong-Hong],
GALD-SE: Guided Anisotropic Lightweight Diffusion for Efficient
Speech Enhancement,
SPLetters(32), 2025, pp. 426-430.
IEEE DOI
2501
Anisotropic, Speech enhancement, Noise, Noise measurement,
Diffusion processes, Estimation, Anisotropic magnetoresistance,
anisotropic diffusion process
BibRef
Hou, Z.[Zhongshu],
Lei, T.[Tong],
Hu, Q.[Qinwen],
Cao, Z.Z.[Zhan-Zhong],
Lu, J.[Jing],
SNR-Progressive Model With Harmonic Compensation for Low-SNR Speech
Enhancement,
SPLetters(32), 2025, pp. 476-480.
IEEE DOI
2501
Harmonic analysis, Signal to noise ratio, Speech enhancement,
Power harmonic filters, Estimation, Spectrogram, Noise measurement,
SNR-progressive learning
BibRef
Jannu, C.[Chaitanya],
Vanambathina, S.D.[Sunny Dayal],
An Overview of Speech Enhancement Based on Deep Learning Techniques,
IJIG(25), No. 1, Januaury 2025, pp. 2550001.
DOI Link
2502
BibRef
Zhou, H.[Hao],
Zhou, Y.[Yi],
Cheng, Z.H.[Zhen-Hua],
Zhao, Y.[Yu],
Liu, Y.[Yin],
Improved Encoder-Decoder Architecture With Human-Like Perception
Attention for Monaural Speech Enhancement,
SPLetters(32), 2025, pp. 1670-1674.
IEEE DOI
2505
Decoding, Speech enhancement, Noise measurement, Convolution, Noise,
Dams, Training, Noise reduction, Computational modeling, attention mechanism
BibRef
Yechuri, S.[Sivaramakrishna],
Vanabathina, S.D.[Sunny Dayal],
Speech Enhancement: A Review of Different Deep Learning Methods,
IJIG(25), No. 3, May 2025, pp. 2550024.
DOI Link
2505
BibRef
Lei, Y.[Yue],
Luo, X.[Xucheng],
Tai, W.X.[Wen-Xin],
Zhou, F.[Fan],
Progressive Skip Connection Improves Consistency of Diffusion-Based
Speech Enhancement,
SPLetters(32), 2025, pp. 1650-1654.
IEEE DOI
2505
Training, Speech enhancement, Gaussian noise, Mutual information,
Diffusion processes, Predictive models, Noise measurement,
mutual information
BibRef
Xu, S.[Shiyun],
Cao, Y.H.[Ying-Han],
Zhang, W.J.[Wen-Jie],
Zhang, Z.[Zehua],
Wang, M.J.[Ming-Jiang],
FSTF-AN: Fused Sparse Temporal-Frequency Attentive Network for
Multi-Channel Speech Enhancement,
SPLetters(32), 2025, pp. 2124-2128.
IEEE DOI
2506
Feature extraction, Speech enhancement, Transformers, Convolution,
Spatial databases, Reverberation, Time-frequency analysis,
multi-scale feed-forward network
BibRef
Ma, H.[Hao],
Chen, R.[Rujin],
Zhang, X.L.[Xiao-Lei],
Liu, J.[Ju],
Li, X.L.[Xue-Long],
Enhancing Intelligibility for Generative Target Speech Extraction via
Joint Optimization With Target Speaker ASR,
SPLetters(32), 2025, pp. 2309-2313.
IEEE DOI
2506
Speech enhancement, Training, Decoding, Spectrogram, Measurement,
Semantics, Predictive models, Computational modeling, Vectors,
multi-task joint learning
BibRef
Sadeghi, M.[Mostafa],
Ayilo, J.E.[Jean-Eudes],
Serizel, R.[Romain],
Alameda-Pineda, X.[Xavier],
Posterior Transition Modeling for Unsupervised Diffusion-Based Speech
Enhancement,
SPLetters(32), 2025, pp. 2694-2698.
IEEE DOI
2507
Noise measurement, Speech enhancement, Training,
Diffusion processes, Diffusion models,
conditional transition modeling
BibRef
Yang, D.H.[Da-Hee],
Lee, J.[Jaeuk],
Chang, J.H.[Joon-Hyuk],
Tokenized Generative Speech Enhancement With Language Model and Flow
Matching,
SPLetters(32), 2025, pp. 2828-2832.
IEEE DOI
2508
Spectrogram, Noise measurement, Speech enhancement, Tokenization, Decoding,
Training, Noise, Indexes, Computational modeling, Acoustics, flow-matching
BibRef
Han, Y.[Yi],
Chen, H.[Hang],
Liu, L.J.[Li-Juan],
Du, J.[Jun],
Dual-Branch Codec With Orthogonality Constraint and Knowledge
Distillation for Noisy Environment,
SPLetters(32), 2025, pp. 3017-3021.
IEEE DOI
2509
Noise measurement, Noise, Codecs, Quantization (signal), Training,
Speech enhancement, Speech coding, Decoding, Signal to noise ratio
BibRef
Hua, H.[Hua],
Shang, Z.Q.[Zeng-Qiang],
Li, X.[Xuyuan],
Yang, C.[Chen],
Zhang, P.Y.[Peng-Yuan],
Flexpéro: Flexible Expressive Zero-Shot Speech Refinement via
In-Context Learning,
SPLetters(32), 2025, pp. 3122-3126.
IEEE DOI
2509
Training, Speech enhancement, Mathematical models, Phonetics,
Acoustics, Timbre, Data mining, Controllability, Context modeling,
zero-shot speech generation
BibRef
Wang, H.Y.[Hao-Yu],
Qiang, C.Y.[Chun-Yu],
Wang, T.R.[Tian-Rui],
Gong, C.[Cheng],
Wang, L.B.[Long-Biao],
Emotional Style Transfer With Intensity Control in Zero-Shot TTS,
SPLetters(32), 2025, pp. 3137-3141.
IEEE DOI
2509
Standards, Training, Data mining, Speech enhancement,
Computer architecture, Vocoders, Timbre, Text to speech,
cross speaker
BibRef
Cheong, S.[Sein],
Kim, M.[Minseung],
Shin, J.W.[Jong Won],
Integrated DNN-Based Parameter Estimation for Multichannel Speech
Enhancement,
SPLetters(32), 2025, pp. 3320-3324.
IEEE DOI
2509
Parameter estimation, Noise, Array signal processing, Estimation,
Speech enhancement, Microphones, Noise measurement,
DNN-based parameter estimation
BibRef
Jiang, W.B.[Wen-Bin],
Wen, F.[Fei],
Yu, K.[Kai],
MOS-GAN: Mean Opinion Score GAN for Unsupervised Speech Enhancement,
SPLetters(32), 2025, pp. 3465-3469.
IEEE DOI
2510
Speech enhancement, Training, Measurement, Noise measurement,
Generators, Unsupervised learning, Training data, Data models,
unsupervised learning
BibRef
Dmitrieva, E.[Ekaterina],
Kaledin, M.[Maksim],
HiFi-Stream: Streaming Speech Enhancement With Generative Adversarial
Networks,
SPLetters(32), 2025, pp. 3595-3599.
IEEE DOI
2510
Computational modeling, Frequency modulation, Convolution,
Speech enhancement, Spectrogram, Training, Generators,
streaming audio processing
BibRef
Li, X.S.[Xin-Shu],
Tan, Z.H.[Zhen-Hua],
Xia, Z.C.[Zhen-Che],
Wu, D.[Danke],
Zhang, B.[Bin],
Single-Channel Speech Separation Focusing on Attention DE,
ICPR22(3204-3209)
IEEE DOI
2212
Training, Convolution, Particle separators, Focusing,
Speech recognition, Speech enhancement, Feature extraction,
SepFormer Block
BibRef
Xu, X.M.[Xin-Meng],
Hao, J.J.[Jian-Jun],
U-Former: Improving Monaural Speech Enhancement with Multi-head Self
and Cross Attention,
ICPR22(663-369)
IEEE DOI
2212
Training, Time-frequency analysis, Target tracking,
Neural networks, Speech recognition, Speech enhancement,
multi-head cross-attention
BibRef
Li, D.S.[Deng-Shi],
Zhao, L.X.[Lan-Xin],
Xiao, J.[Jing],
Liu, J.Q.[Jia-Qi],
Guan, D.Z.[Duan-Zheng],
Wang, Q.R.[Qian-Rui],
Adaptive Speech Intelligibility Enhancement for Far-and-Near-end Noise
Environments Based on Self-attention StarGAN,
MMMod22(II:205-217).
Springer DOI
2203
BibRef
Xiao, J.[Jing],
Liu, J.Q.[Jia-Qi],
Li, D.S.[Deng-Shi],
Zhao, L.X.[Lan-Xin],
Wang, Q.R.[Qian-Rui],
Speech Intelligibility Enhancement By Non-Parallel Speech Style
Conversion Using CWT and iMetricGAN Based CycleGAN,
MMMod22(I:544-556).
Springer DOI
2203
BibRef
Hegde, S.B.[Sindhu B.],
Prajwal, K.R.,
Mukhopadhyay, R.[Rudrabha],
Namboodiri, V.[Vinay],
Jawahar, C.V.,
Visual Speech Enhancement Without A Real Visual Stream,
WACV21(1925-1934)
IEEE DOI
2106
Visualization, Lips, Speech enhancement, Streaming media,
Filtering algorithms, Information filters, Noise measurement
BibRef
Sun, Z.B.[Zhong-Bo],
Wang, Y.N.[Yan-Nan],
Cao, L.[Li],
An Attention Based Speaker-independent Audio-visual Deep Learning Model
for Speech Enhancement,
MMMod20(II:722-728).
Springer DOI
2003
BibRef
Wang, Y.,
Research Progress in Speech Enhancement Technology,
CVIDL20(222-226)
IEEE DOI
2102
neural nets, speech enhancement,
abstract original speech signals, pure speech signals,
deep learning
BibRef
Dendani, B.[Bilal],
Bahi, H.[Halima],
Sari, T.[Toufik],
Speech Enhancement Based on Deep Autoencoder for Remote Arabic Speech
Recognition,
ICISP20(221-229).
Springer DOI
2009
BibRef
Coto-Jiménez, M.[Marvin],
Experimental Study on Transfer Learning in Denoising Autoencoders for
Speech Enhancement,
MCPR20(307-317).
Springer DOI
2007
BibRef
Zhang, R.[Rui],
Hu, R.M.[Rui-Min],
Li, G.[Gang],
Wang, X.C.[Xiao-Chen],
Spectral Tilt Estimation for Speech Intelligibility Enhancement Using
RNN Based on All-Pole Model,
MMMod19(II:144-156).
Springer DOI
1901
BibRef
Samui, S.[Suman],
Chakrabarti, I.[Indrajit],
Ghosh, S.K.[Soumya K.],
Improving the Performance of Deep Learning Based Speech Enhancement
System Using Fuzzy Restricted Boltzmann Machine,
PReMI17(534-542).
Springer DOI
1711
BibRef
Pignotti, A.[Alessio],
Marcozzi, D.[Daniele],
Cifani, S.[Simone],
Squartini, S.[Stefano],
Piazza, F.[Francesco],
A Blind Source Separation Based Approach for Speech Enhancement in
Noisy and Reverberant Environment,
COST08(356-367).
Springer DOI
0810
BibRef
Kuhnapfel, T.[Thorsten],
Tan, T.[Tele],
Venkatesh, S.[Svertha],
Igel, B.[Burkhard],
Distributed Audio Network for Speech Enhancement in Challenging Noise
Backgrounds,
AVSBS09(308-313).
IEEE DOI
0909
BibRef
Kuhnapfel, T.[Thorsten],
Tan, T.[Tele],
Venkatesh, S.[Svetha],
Nordholm, S.E.[Sven Erik],
Igel, B.[Burkhard],
Adaptive speech enhancement with varying noise backgrounds,
ICPR08(1-4).
IEEE DOI
0812
BibRef
Li, W.H.[Wei-Hong],
Liu, M.[Ming],
Zhu, Z.G.[Zhi-Gang],
Huang, T.S.[Thomas S.],
LDV Remote Voice Acquisition and Enhancement,
ICPR06(IV: 262-265).
IEEE DOI
0609
BibRef
Chapter on New Unsorted Entries, and Other Miscellaneous Papers continues in
Speech Synthesis, Synthetic Speech .