Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Deep Recurrent Neural Networks for Source Separation
| Date | Stars |
|---|---|
| 2026-07-31 | 371 |
| 2026-08-05 | 371 |
| 2026-08-06 | 371 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# Deep Learning For Monaural Source Separation ## Demo Webpage: https://sites.google.com/site/deeplearningsourceseparation/ ## Experiments ### MIR-1K experiment (singing voice separation) 1. Training code: ```codes/mir1k/train_mir1k_demo.m``` 2. Demo - Download a trained model ```http://www.ifp.illinois.edu/~huang146/DNN_separation/model_400.mat``` - Put the model at ```codes/mir1k/demo``` and go to the folder - Run: ```codes/mir1k/demo/run_test_single_model.m``` ### TIMIT experiment (speech separation) 1. Training code: ```codes/timit/train_timit_demo.m``` and ```codes/timit/train_timit_demo_mini_clip.m``` 2. Demo - Download a trained model ```http://www.ifp.illinois.edu/~huang146/DNN_separation/timit_model_70.mat``` - Put the model at ```codes/timit/demo``` and go to the folder - Run: ```codes/timit/demo/run_test_single_model.m``` ### TSP experiment (speech separation) 1. Training code: ```codes/TSP/train_TSP_demo_mini_clip.m``` 2. Demo - Download a trained model ```http://www.ifp.illinois.edu/~huang146/DNN_separation/TSP_model_RNN1_win1_h300_l2_r0_64ms_1000000_softabs_linearout_RELU_logmel_trn0_c1e-10_c0.001_bsz100000_miter10_bf50_c0_d0_7650.mat``` - Put the model at ```codes/TSP/demo``` and go to the folder - Run the demo code at ```codes/TSP/demo/run_test_single_model.m``` ### Denosing experiment 1. Put original ```FCJF0, FDAW0', FDML0, FECD0, 'FETB0', 'FJSP0', 'FKFB0', 'FMEM0', 'FSAH0', 'FSJK1', 'FSMA0', 'FTBR0', 'FVFB0' 'FVMH0``` of the original TIMIT data under ```codes/denoising/Data/timit/``` 2. Training code: ```codes/denoising/train_denoising_demo.m``` 3. Demo - Download a trained model ```http://www.ifp.illinois.edu/~huang146/DNN_separation/denoising_model_870.mat``` - Put the model at ```codes/denoising/demo``` and go to the folder - Run the demo code at ```codes/denoising/demo/run_test_single_model.m``` ## Dependencies 1. The package is modified based on [rnn-speech-denoising](https://github.com/amaas/rnn-speech-denoising) 2. The software depends on Mark Schmidt's [minFunc](http://www.di.ens.fr/~mschmidt/Software/minFunc.html) package for convex optimization. 3. Additionally, we have included Mark Hasegawa-Johnson's [HTK write and read functions](http://www.isle.illinois.edu/sst/software) that are used to handle the MFCC files. 4. We use [HTK](http://htk.eng.cam.ac.uk) for computing features (MFCC, logmel) (HCopy). 5. We use signal processing functions from [labrosa](http://labrosa.ee.columbia.edu/). 6. We use [BSS Eval](http://bass-db.gforge.inria.fr/bss_eval/) toolbox Version 2.0, 3.0 for evaluation. 7. We use [MIR-1K](https://sites.google.com/site/unvoicedsoundseparation/mir-1k) for singing voice separation task. 8. We use [TSP](http://www-mmsp.ece.mcgill.ca/Documents/Data/) for speech separation task. ## Work on your data: 1. To try the codes on your data, see mir1k, TSP settings - put your data into ```codes/mir1k/Wavfile``` or ```codes/TSP/Data/``` accordingly. 2. Look at the unit test parameters below ```codes/mir1k/train_mir1k_demo.m```, ```codes/TSP/train_TSP_demo_mini_clip.m``` (with minibatch lbfgs, gradient clipping) 3. Tune the parameters on the dev set and check the results. ## Reference 1. P.-S. Huang, M. Kim, M. Hasegawa-Johnson, P. Smaragdis, "[Joint Optimization of Masks and Deep Recurrent Neural Networks for Monaural Source Separation](http://posenhuang.github.io/papers/Joint_Optimization_of_Masks_and_Deep%20Recurrent_Neural_Networks_for_Monaural_Source_Separation_TASLP2015.pdf)", IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 23, no. 12, pp. 2136–2147, Dec. 2015 2. P.-S. Huang, M. Kim, M. Hasegawa-Johnson, P. Smaragdis, "[Singing-Voice Separation From Monaural Recordings Using Deep Recurrent Neural Networks](http://posenhuang.github.io/papers/DRNN_ISMIR2014.pdf)," in International Society for Music Information Retrieval Conference (ISMIR) 2014. 3. P.-S. Huang, M. Kim, M. Hasegawa-Johnson, P. Smaragdis, "[Deep Learning for M
Excerpt of 4,335 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:06bf0791f84d6225, topic:deep-learning