DeepSeparation
Keras Implementation and Experiments with Joint Optimization of Masks and Deep Recurrent Neural Networks for Source Separation
Using a custom designed keras layer for time frequency masking
Project under development
Dependencies
- python 3.x,
- keras2.x
- SciPy
- musdb
Usage
In configuration.py, set data_dir to folder containing test files and results_dir to output folder and run test.py
Parameters tested
Input shape: This is dependent on the sampleing rate, for DSD100 rate of 44.1 kHz, one second of audio, scipy fft by default will make 513 bins. Sequence length of 4 makes the input shape [N,513,4]
Number of LSTM layers: 3,2,1
Uints per layer: 256,512
Activation funciton: ReLu, tanh
L2 regularization on recurrent layers: 0.0 1.0
Batch normalization: yes and no
Loss = mse + [reg const]discriminative reg const : 0,0.5,1
Callbacks
Writing on tensorboard, early stopping and reduce learning rate on plateau
Reference
- 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