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Fast and stable blind source separation with rank-1 updates

Abstract

We propose a new algorithm for the blind source separation of acoustic sources. This algorithm is an alternative to the popular auxiliary function based independent vector analysis using iterative projection (AuxIVA-IP). It optimizes the same cost function, but instead of alternate updates of the rows of the demixing matrix, we propose a sequence of rank-1 updates. Remarkably, and unlike the previous method, the resulting updates do not require matrix inversion. Moreover, their computational complexity is quadratic in the number of microphones, rather than cubic in AuxIVA-IP. In addition, we show that the new method can be derived as alternate updates of the steering vectors of sources. Accordingly, we name the method iterative source steering (AuxIVA-ISS). Finally, we confirm in simulated experiments that the proposed algorithm separates sources just as well as AuxIVA-IP, at a lower computational cost.

Authors

Install

We use anaconda to setup the Python environment.

git clone --recursive <url>
cd piva
conda env create -f environment.yml
conda activate piva
python setup.py build_ext --inplace
cd ..

Run Experiments

The two experiments presented in the paper can be run by the following steps. This produces two files ./experiment_metrics_speed_results.json ./experiment_speed_11_17_results.json that are later used to produce the plots.

conda activate piva

# Run the simulations
python ./experiment_metrics_speed.py
python ./experiment_speed_11_17.py

# Plot the results
python ./make_figures.py ./experiment_metrics_speed_results.json ./experiment_speed_11_17_results.json

The two simulation output data files produced for the figures in the paper were kept in the sim_results folder.

License

The code in this repository is released under the MIT license.

About

Code to reproduce the experiments in the paper "Fast and stable blind source separation with rank-1 updates" presented at ICASSP 2020.

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