├── LICENSE ├── .gitignore └── README.md /LICENSE: -------------------------------------------------------------------------------- 1 | This is free and unencumbered software released into the public domain. 2 | 3 | Anyone is free to copy, modify, publish, use, compile, sell, or 4 | distribute this software, either in source code form or as a compiled 5 | binary, for any purpose, commercial or non-commercial, and by any 6 | means. 7 | 8 | In jurisdictions that recognize copyright laws, the author or authors 9 | of this software dedicate any and all copyright interest in the 10 | software to the public domain. We make this dedication for the benefit 11 | of the public at large and to the detriment of our heirs and 12 | successors. 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used by e.g. github.com/David-OConnor/pyflow 95 | __pypackages__/ 96 | 97 | # Celery stuff 98 | celerybeat-schedule 99 | celerybeat.pid 100 | 101 | # SageMath parsed files 102 | *.sage.py 103 | 104 | # Environments 105 | .env 106 | .venv 107 | env/ 108 | venv/ 109 | ENV/ 110 | env.bak/ 111 | venv.bak/ 112 | 113 | # Spyder project settings 114 | .spyderproject 115 | .spyproject 116 | 117 | # Rope project settings 118 | .ropeproject 119 | 120 | # mkdocs documentation 121 | /site 122 | 123 | # mypy 124 | .mypy_cache/ 125 | .dmypy.json 126 | dmypy.json 127 | 128 | # Pyre type checker 129 | .pyre/ 130 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Awesome-Speech-Enhancement 2 | A collection of trending speech enhancement papers 3 | 4 | 5 | | Year | Title | Publication | Code | 6 | | :-----| :----: | :----: | :----: | 7 | | 2019 | [An Attention-based Neural Network Approach For Single Channel Speech Enhancement](http://lxie.nwpu-aslp.org/papers/2019ICASSP-XiangHao.pdf) | IEEE-ICASSP | [github](https://github.com/chanil1218/Attention-SE.pytorch) | 8 | | 2019 | [Learning with Learned Loss Function: Speech Enhancement with Quality-Net to Improve Perceptual Evaluation of Speech Quality](https://arxiv.org/pdf/1905.01898.pdf) | IEEE-SPL | | 9 | | 2019 | [Speech Enhancement Based on A New Architecture of Wasserstein Generative Adversarial Networks](https://ieeexplore.ieee.org/document/8706647) | IEEE-ISCSLP | | 10 | | 2018 | [Quality-Net: An End-to-End Non-intrusive Speech Quality Assessment Model based on BLSTM](https://arxiv.org/pdf/1808.05344.pdf) | arxiv | [github](https://github.com/JasonSWFu/Quality-Net) | 11 | | 2018 | [Perceptually Guided Speech Enhancement Using Deep Neural Networks](https://cliffzhao.github.io/Publications/ZXGZ.icassp18.pdf) | IEEE-ICASSP | | 12 | | 2018 | [Supervised Speech Separation Based on Deep Learning: An Overview](https://arxiv.org/pdf/1708.07524) | IEEE-TASLP | | 13 | | 2017 | [SEGAN: Speech Enhancement Generative Adversarial Network](https://arxiv.org/pdf/1703.09452) | arxiv | [github](https://github.com/santi-pdp/segan) | 14 | | 2016 | [Investigating RNN-based speech enhancement methods for noise-robust Text-to-Speech](https://www.research.ed.ac.uk/portal/files/26581510/SSW9_Cassia_1.pdf) | [ISCA-SSW](https://www.isca-speech.org/archive/SSW_2016/pdfs/ssw9_PS2-4_Valentini-Botinhao.pdf) | | 15 | | 2016 | [Wavenet: A Generative Model For Raw Audio](https://arxiv.org/pdf/1609.03499) | arxiv | [github](https://github.com/ibab/tensorflow-wavenet) | 16 | | 2014 | [On Training Targets for Supervised Speech Separation](https://web.cse.ohio-state.edu/~wang.77/papers/WNW.taslp14.pdf) | ACM-TASLP | | 17 | | 2010 | [A short-time objective intelligibility measure for time-frequency weighted noisy speech](http://cas.et.tudelft.nl/pubs/Taal2010.pdf) | IEEE-ICASSP | | 18 | | 2001 | [Perceptual evaluation of speech quality (PESQ)-a new method for speech quality assessment of telephone networks and codecs](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.5.9136&rep=rep1&type=pdf) | IEEE-ICASSP | | 19 | | 1984 | [Speech Enhancement Using a-Minimum Mean- Square Error Short-Time Spectral Amplitude Estimator](https://ieeexplore.ieee.org/abstract/document/1164453/) | IEEE-TASSP | | 20 | | 1980 | [Speech Enhancement Using a Soft-Decision Noise Suppression Filter](https://ieeexplore.ieee.org/iel6/29/26145/01163394.pdf) | IEEE-TASSP 21 | 22 | 23 | ## Data sets 24 | 25 | | Year | Title | Link | 26 | | :--: | :--: | :--: | 27 | | 2017 | [ESC-50: Dataset for Environmental Sound Classification](https://github.com/karoldvl/paper-2015-esc-dataset) | [github](https://github.com/karolpiczak/ESC-50) | 28 | | 2015 | [MUSAN: A Music, Speech, and Noise Corpus](https://arxiv.org/pdf/1510.08484.pdf) | [OpenSLR](http://www.openslr.org/17/) | 29 | | 2013 | The voice bank corpus: Design, collection and data analysis of a large regional accent speech database | | 30 | | 2013 | The Diverse Environments Multi-channel Acoustic Noise Database (DEMAND): A database of multichannel environmental noise recordings | [zenodo](https://zenodo.org/record/1227121#.XoxK59MzbRY) [kaggle](https://www.kaggle.com/aanhari/demand-dataset) | 31 | | 1996 | [NOISEX-92](http://mi.eng.cam.ac.uk/comp.speech/Section1/Data/noisex.html) | | 32 | | 1993 | [Assessment for automatic speech recognition: II. NOISEX-92: A database and an experiment to study the effect of additive noise on speech recognition systems](https://www.sciencedirect.com/science/article/abs/pii/0167639393900953) | [CMU-DSP](http://www.speech.cs.cmu.edu/comp.speech/Section1/Data/noisex.html) | 33 | 34 | ## Materials 35 | | Title | Link | 36 | | :--: | :--: | 37 | |Speech Technology: A Practical Introduction Topic: Spectrogram, Cepstrum and Mel-Frequency Analysis | [CMU](http://www.speech.cs.cmu.edu/15-492/slides/03_mfcc.pdf) | 38 | | About Transformer Networks | [blog](http://jalammar.github.io/illustrated-transformer/) | --------------------------------------------------------------------------------