scatterbrain333 / dr-nmf Goto Github PK

DR-NMF is a recurrent neural network constructed from the unfolded iterations of the iterative soft-thresholding algorithm (ISTA) applied to sparse NMF inference. Sparse NMF inference is the task of inferring the nonnegative sparse coefficients H given a nonnegative dictionary W such that WH approximates a nonnegative observation matrix X. For speech separation, the observation matrix X is the raw spectrogram of noisy audio, and the dictionary W is partitioned into speech and noise components. This partitioning of the dictionary W allows computation of an enhancement mask in the STFT domain.

Read the paper here: https://arxiv.org/abs/1709.07124

Uses the task 2 data from the 2nd CHiME Challenge.

1. Download required toolboxes by running download_toolboxes.sh.
2. Generate taskfiles by replacing the variable chime2_path in create_taskfiles.sh by your local CHiME2 path and running create_taskfiles.sh.
3. Use enhance.py to train, reconstruct, and score audio.

Uses code from the following sources, which are automatically downloaded and unzipped by download_toolboxes.sh:

• sparseNMF by Jonathan Le Roux from http://www.jonathanleroux.org/software/sparseNMF.zip (put Matlab files in "sparseNMF" directory)
• BSS Eval by Emmanuel Vincent from http://bass-db.gforge.inria.fr/bss_eval/bss_eval.zip (put "bss-eval" directory in "evaluation" directory)
• Matlab PESQ implementation by Y. Hu and P. Loizou from http://ecs.utdallas.edu/loizou/speech/composite.zip (put "composite" directory in "evaluation" directory)
• Matlab STOI implementation by Cees Taal from http://ceestaal.nl/stoi.zip (put "stoi" directory in "evaluation" directory)
• Matlab Voicebox toolbox by Mike Brookes from http://www.ee.ic.ac.uk/hp/staff/dmb/voicebox/voicebox.zip (put "voicebox" directory in "evaluation" directory)