dxmsjtu / 2020_interspeech_gmdp

Generalized Minimal Distortion Principle for Blind Source Separation

Generalized Minimal Distortion Principle for Blind Source Separation

Abstract

We revisit the source image estimation problem from blind source separation (BSS). We generalize the traditional minimum distortion principle to maximum likelihood estimation with a model for the residual spectrograms. Because residual spectrograms typically contain other sources, we propose to use a mixed-norm model that lets us finely tune sparsity in time and frequency. We propose to carry out the minimization of the mixed-norm via majorization-maximization optimization, leading to an iteratively reweighted least-squares algorithm. The algorithm balances well efficiency and ease of implementation. We assess the performance of the proposed method as applied to two well-known determined BSS and one joint BSS-dereverberation algorithms. We find out that it is possible to tune the parameters to improve separation by up to 2 dB, with no increase in distortion, and at little computational cost. The method thus provides a cheap and easy way to boost the performance of blind source separation.

Author

Robin Scheibler ([email protected])

Run the experiments

The easiest is to rely on anaconda or miniconda for the installation.

We use ipyparallel to parallelize the experiment.

Setup

# prepare the environment
git clone --recursive https://github.com/fakufaku/2020_interspeech_gdmp.git
cd 2020_interspeech_gdmp
conda env create -f environment.yml
conda activate gmdp

# generate the dataset
cd bss_speech_dataset
python ../config_dataset.json
cd ..

Run the Experiments

# start the engines
ipcluster start --daemonize

# run experiment for AuxIVA and ILRMA
python ./paper_simulation.py ./experiment1_config.json

# run experiment for ILRMA-T
python ./paper_simulation.py ./experiment2_config.json

# stop the engines
ipcluster stop

Create the Tables and Figures

In general, do the following

python ./analysis.py ./sim_results/<results_folder>

To recreate the figures with the simulation results used in the paper do

python ./analysis.py ./sim_results/20200511-112906_experiment1_config_102af93240
python ./analysis.py ./sim_results/20200507-012736_experiment2_config_102af93240

License

The code is released under MIT License.