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#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. 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

2. The software depends on Mark Schmidt's minFunc package for convex optimization.

3. Additionally, we have included Mark Hasegawa-Johnson's HTK write and read functions that are used to handle the MFCC files.

4. We use HTK for computing features (MFCC, logmel) (HCopy).

5. We use signal processing functions from labrosa.

6. We use BSS Eval toolbox Version 2.0, 3.0 for evaluation.

7. We use MIR-1K for singing voice 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", 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," in International Society for Music Information Retrieval Conference (ISMIR) 2014.

3. P.-S. Huang, M. Kim, M. Hasegawa-Johnson, P. Smaragdis, "Deep Learning for Monaural Speech Separation," in IEEE International Conference on Acoustic, Speech and Signal Processing 2014.

##License Apache License. For commercial use, please contact me.