Skip to content
Switch branches/tags

Latest commit


Git stats


Failed to load latest commit information.

Independent Vector Analysis with more Microphones than Sources

This repository provides implementations and code to reproduce the results of the paper

R. Scheibler and N. Ono, "Independent Vector Analysis with more Microphones than Sources," 2019.


We extend frequency-domain blind source separation based on independent vector analysis to the case where there are more microphones than sources. The signal is modelled as non-Gaussian sources in a Gaussian background. The proposed algorithm is based on a parametrization of the demixing matrix decreasing the number of parameters to estimate. Furthermore, orthogonal constraints between the signal and background subspaces are imposed to regularize the separation. The problem can then be posed as a constrained likelihood maximization. We propose efficient alternating updates guaranteed to converge to a stationary point of the cost function. The performance of the algorithm is assessed on simulated signals. We find that the separation performance is on par with that of the conventional determined algorithm at a fraction of the computational cost.


Robin Scheibler and Nobutaka Ono are with the Faculty of System Design at Tokyo Metropolitan University.


Robin Scheibler (robin[at]tmu[dot]ac[dot]jp)
6-6 Asahigaoka
Hino, Tokyo
191-0065 Japan


The preferred way to run the code is using anaconda. An environment.yml file is provided to install the required dependencies.

# create the minimal environment
conda env create -f environment.yml

# switch to new environment
conda activate 2019_scheibler_overiva

Test OverIVA

The algorithm can be tested and compared to others using the sample script It can be run as follows.

$ python ./ --help
usage: [-h] [--no_cb] [-b BLOCK]
                          [-a {auxiva,auxiva_pca,overiva,ilrma,ogive}]
                          [-d {laplace,gauss}] [-i {eye,eig}] [-m MICS]
                          [-s SRCS] [-n N_ITER] [--gui] [--save]

Demonstration of blind source separation using IVA.

optional arguments:
  -h, --help            show this help message and exit
  --no_cb               Removes callback function
  -b BLOCK, --block BLOCK
                        STFT block size
  -a {auxiva,auxiva_pca,overiva,ilrma,ogive}, --algo {auxiva,auxiva_pca,overiva,ilrma,ogive}
                        Chooses BSS method to run
  -d {laplace,gauss}, --dist {laplace,gauss}
                        IVA model distribution
  -i {eye,eig}, --init {eye,eig}
                        Initialization, eye: identity, eig: principal
  -m MICS, --mics MICS  Number of mics
  -s SRCS, --srcs SRCS  Number of sources
  -n N_ITER, --n_iter N_ITER
                        Number of iterations
  --gui                 Creates a small GUI for easy playback of the sound
  --save                Saves the output of the separation to wav files

For example, we can run overiva with 4 microphones and 2 sources.

python ./ -a overiva -m 4 -s 2

Reproduce the Results

The code can be run serially, or using multiple parallel workers via ipyparallel. Moreover, it is possible to only run a few loops to test whether the code is running or not.

  1. Run test loops serially

     python ./ ./overiva_sim_config.json -t -s
  2. Run test loops in parallel

     # start workers in the background
     # N is the number of parallel process, often "# threads - 1"
     ipcluster start --daemonize -n N
     # run the simulation
     python ./ ./overiva_sim_config.json -t
     # stop the workers
     ipcluster stop
  3. Run the whole simulation

     # start workers in the background
     # N is the number of parallel process, often "# threads - 1"
     ipcluster start --daemonize -n N
     # run the simulation
     python ./ ./overiva_sim_config.json
     # stop the workers
     ipcluster stop

The results are saved in a new folder data/<data>-<time>_overiva_sim_<flag_or_hash> containing the following files

parameters.json  # the list of global parameters of the simulation
arguments.json  # the list of all combinations of arguments simulated
data.json  # the results of the simulation

Figure 2. and 3. from the paper are produced then by running

python ./ data/<data>-<time>_overiva_sim_<flag_or_hash> -s


For the experiment, we concatenated utterances from the CMU ARCTIC speech corpus to obtain samples of at least 15 seconds long. The dataset thus created was stored on zenodo with DOI 10.5281/zenodo.3066488. The data is automatically retrieved upon running the scripts, but can also be manually downloaded with the script.

python ./

It is stored in the samples directory.

Use OverIVA

Our implementation of the proposed OverIVA algorithm lives in the file It can be used simply like this.

from overiva import overiva

# STFT tensor, a numpy.ndarray with shape (frames, frequencies, channels)
X = ...

# perform separation, output Y has the same shape as X
Y = overiva(X, n_src=2)

The function comes with docstrings.

overiva(X, n_src=None, n_iter=20, proj_back=True, W0=None, model="laplace",
        init_eig=False, return_filters=False, callback=None,)

Implementation of overdetermined IVA algorithm for BSS as presented. See
the following publication for a detailed description of the algorithm.

R. Scheibler and N. Ono, Independent Vector Analysis with more Microphones than Sources, arXiv, 2019.

X: ndarray (nframes, nfrequencies, nchannels)
    STFT representation of the signal
n_src: int, optional
    The number of sources or independent components. When
    ``n_src==nchannels``, the algorithms is identical to AuxIVA. When
    ``n_src==1``, then it is doing independent vector extraction.
n_iter: int, optional
    The number of iterations (default 20)
proj_back: bool, optional
    Scaling on first mic by back projection (default True)
W0: ndarray (nfrequencies, nsrc, nchannels), optional
    Initial value for demixing matrix
model: str
    The model of source distribution 'gauss' or 'laplace' (default)
init_eig: bool, optional (default ``False``)
    If ``True``, and if ``W0 is None``, then the weights are initialized
    using the principal eigenvectors of the covariance matrix of the input
return_filters: bool
    If true, the function will return the demixing matrix too
callback: func
    A callback function called every 10 iterations, allows to monitor

Returns an (nframes, nfrequencies, nsources) array. Also returns
the demixing matrix (nfrequencies, nchannels, nsources)
if ``return_values`` keyword is True.

Summary of the Files in this Repo

environment.yml  # anaconda environment file  # implementation of AuxIVA with PCA dim reduction step  # implementation of orthogonally constrained independent vector extraction (OGIVE)  # implementation of the proposed overdetermined IVA  # script that gets the data necessary for the experiment  # contains a bunch of helper routines for the simulation  # test file for source separation, with audible output  # script to run exhaustive simulation, used for the paper
overiva_sim_config.json  # simulation configuration file  # plots the figures from the output of

data  # directory containing simulation results
rrtools  # tools for parallel simulation


Code to do blind source separation with more microphones than sources using auxilliary based independent vector analysis.





No releases published


No packages published