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Title: GitHub - JihwanParkPreprocessing/DeepXi: Deep Xi: A deep learning approach to a priori SNR estimation implemented in TensorFlow 2/Keras. For speech enhancement and robust ASR.

Open Graph Title: GitHub - JihwanParkPreprocessing/DeepXi: Deep Xi: A deep learning approach to a priori SNR estimation implemented in TensorFlow 2/Keras. For speech enhancement and robust ASR.

X Title: GitHub - JihwanParkPreprocessing/DeepXi: Deep Xi: A deep learning approach to a priori SNR estimation implemented in TensorFlow 2/Keras. For speech enhancement and robust ASR.

Description: Deep Xi: A deep learning approach to a priori SNR estimation implemented in TensorFlow 2/Keras. For speech enhancement and robust ASR. - JihwanParkPreprocessing/DeepXi

Open Graph Description: Deep Xi: A deep learning approach to a priori SNR estimation implemented in TensorFlow 2/Keras. For speech enhancement and robust ASR. - JihwanParkPreprocessing/DeepXi

X Description: Deep Xi: A deep learning approach to a priori SNR estimation implemented in TensorFlow 2/Keras. For speech enhancement and robust ASR. - JihwanParkPreprocessing/DeepXi

Opengraph URL: https://github.com/JihwanParkPreprocessing/DeepXi

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READMEhttps://patch-diff.githubusercontent.com/JihwanParkPreprocessing/DeepXi
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https://patch-diff.githubusercontent.com/JihwanParkPreprocessing/DeepXi#deep-xi-a-deep-learning-approach-to-a-priori-snr-estimation-for-speech-enhancement
https://patch-diff.githubusercontent.com/JihwanParkPreprocessing/DeepXi#news
Masked Multi-Head Self-Attention for Causal Speech Enhancementhttps://protect-au.mimecast.com/s/E9oJCMwvOEhqv5wBOuJmmiO?domain=authors.elsevier.com
Spectral distortion level resulting in a just-noticeable difference between an a priori signal-to-noise ratio estimate and its instantaneous casehttps://doi.org/10.1121/10.0002113
On Training Targets for Deep Learning Approaches to Clean Speech Magnitude Spectrum Estimationhttps://www.techrxiv.org/articles/preprint/On_Training_Targets_for_Deep_Learning_Approaches_to_Clean_Speech_Magnitude_Spectrum_Estimation/13012760/1
https://github.com/anicolson/tgt_20https://github.com/anicolson/tgt_20
https://patch-diff.githubusercontent.com/JihwanParkPreprocessing/DeepXi#contents
Introductionhttps://patch-diff.githubusercontent.com/JihwanParkPreprocessing/DeepXi#introduction
How does Deep Xi work?https://patch-diff.githubusercontent.com/JihwanParkPreprocessing/DeepXi#how-does-deep-xi-work
Current networkshttps://patch-diff.githubusercontent.com/JihwanParkPreprocessing/DeepXi#current-networks
Available modelshttps://patch-diff.githubusercontent.com/JihwanParkPreprocessing/DeepXi#available-models
Resultshttps://patch-diff.githubusercontent.com/JihwanParkPreprocessing/DeepXi#results
DeepMMSEhttps://patch-diff.githubusercontent.com/JihwanParkPreprocessing/DeepXi#deepmmse
Installationhttps://patch-diff.githubusercontent.com/JihwanParkPreprocessing/DeepXi#installation
How to use Deep Xihttps://patch-diff.githubusercontent.com/JihwanParkPreprocessing/DeepXi#how-to-use-deep-xi
Current issues and potential areas of improvementhttps://patch-diff.githubusercontent.com/JihwanParkPreprocessing/DeepXi#current-issues-and-potential-areas-of-improvement
Where can I get a dataset for Deep Xi?https://patch-diff.githubusercontent.com/JihwanParkPreprocessing/DeepXi#where-can-i-get-a-dataset-for-deep-xi
Which audio do I use with Deep Xi?https://patch-diff.githubusercontent.com/JihwanParkPreprocessing/DeepXi#which-audio-do-i-use-with-deep-xi
Citation guidehttps://patch-diff.githubusercontent.com/JihwanParkPreprocessing/DeepXi#citation-guide
https://patch-diff.githubusercontent.com/JihwanParkPreprocessing/DeepXi#introduction
Deep Xihttps://doi.org/10.1016/j.specom.2019.06.002
[1]https://doi.org/10.1016/j.specom.2019.06.002
[2]https://ieeexplore.ieee.org/document/9066933
https://patch-diff.githubusercontent.com/JihwanParkPreprocessing/DeepXi#how-does-deep-xi-work
[1]https://doi.org/10.1016/j.specom.2019.06.002
[2]https://ieeexplore.ieee.org/document/9066933
https://patch-diff.githubusercontent.com/JihwanParkPreprocessing/DeepXi/blob/master/docs/fig_training_example.png
https://patch-diff.githubusercontent.com/JihwanParkPreprocessing/DeepXi/blob/master/docs/fig_xi_dist.png
https://patch-diff.githubusercontent.com/JihwanParkPreprocessing/DeepXi#current-networks
run.sh.https://github.com/anicolson/DeepXi/blob/master/run.sh
[6]https://doi.org/10.1016/j.specom.2020.10.004
[2]https://ieeexplore.ieee.org/document/9066933
[1]https://doi.org/10.1016/j.specom.2019.06.002
https://patch-diff.githubusercontent.com/JihwanParkPreprocessing/DeepXi/blob/master/docs/fig_mhanet.jpg
https://patch-diff.githubusercontent.com/JihwanParkPreprocessing/DeepXi#available-models
mhanet-1.1chttps://github.com/anicolson/DeepXi/blob/master/run.sh
modelhttps://github.com/anicolson/DeepXi/tree/master/model
resnet-1.1nhttps://github.com/anicolson/DeepXi/blob/master/run.sh
modelhttps://github.com/anicolson/DeepXi/tree/master/model
resnet-1.1chttps://github.com/anicolson/DeepXi/blob/master/run.sh
modelhttps://github.com/anicolson/DeepXi/tree/master/model
Deep Xi datasethttps://ieee-dataport.org/open-access/deep-xi-dataset
run.shhttps://github.com/anicolson/DeepXi/blob/master/run.sh
https://patch-diff.githubusercontent.com/JihwanParkPreprocessing/DeepXi#results
herehttp://ssw9.talp.cat/papers/ssw9_PS2-4_Valentini-Botinhao.pdf
Wienerhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=543199
SEGANhttps://arxiv.org/pdf/1703.09452.pdf
WaveNethttps://arxiv.org/pdf/1706.07162.pdf
MMSE-GANhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8462068
Deep Feature Losshttps://arxiv.org/pdf/1806.10522.pdf
Metric-GANhttps://arxiv.org/pdf/1905.04874.pdf
Koizumi2020https://doi.org/10.1109/icassp40776.2020.9053214
T-GSAhttps://ieeexplore.ieee.org/document/9053591
Deep Xi datasethttps://ieee-dataport.org/open-access/deep-xi-dataset
https://patch-diff.githubusercontent.com/JihwanParkPreprocessing/DeepXi#deepmmse
https://patch-diff.githubusercontent.com/JihwanParkPreprocessing/DeepXi#installation
CUDA 10.1https://developer.nvidia.com/cuda-downloads
cuDNN (>= 7.6)https://developer.nvidia.com/cudnn
https://patch-diff.githubusercontent.com/JihwanParkPreprocessing/DeepXi#how-to-use-deep-xi
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https://patch-diff.githubusercontent.com/JihwanParkPreprocessing/DeepXi#current-issues-and-potential-areas-of-improvement
https://patch-diff.githubusercontent.com/JihwanParkPreprocessing/DeepXi#where-can-i-get-a-dataset-for-deep-xi
http://dx.doi.org/10.21227/3adt-pb04http://dx.doi.org/10.21227/3adt-pb04
Deep Xi paperhttps://doi.org/10.1016/j.specom.2019.06.002
http://dx.doi.org/10.21227/0ppr-yy46http://dx.doi.org/10.21227/0ppr-yy46
sethttps://github.com/anicolson/DeepXi/tree/master/set
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https://patch-diff.githubusercontent.com/JihwanParkPreprocessing/DeepXi#naming-convention-in-the-set-directory
Deep Xi datasethttp://dx.doi.org/10.21227/3adt-pb04
https://patch-diff.githubusercontent.com/JihwanParkPreprocessing/DeepXi#citation-guide
A. Nicolson, K. K. Paliwal, Deep learning for minimum mean-square error approaches to speech enhancement, Speech Communication 111 (2019) 44 - 55, https://doi.org/10.1016/j.specom.2019.06.002.https://doi.org/10.1016/j.specom.2019.06.002
Q. Zhang, A. M. Nicolson, M. Wang, K. Paliwal and C. Wang, "DeepMMSE: A Deep Learning Approach to MMSE-based Noise Power Spectral Density Estimation," in IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 28, pp. 1404-1415, 2020, doi: 10.1109/TASLP.2020.2987441.https://ieeexplore.ieee.org/document/9066933
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