Added README files and batch simulation scripts.

README.md

@@ -0,0 +1,249 @@
+FRIDA: FRI-based DOA Estimation for Arbitrary Array Layout
+==================================
+
+This repository contains all the code to reproduce the results of the paper
+[*FRIDA: FRI-based DOA Estimation for Arbitrary Array Layout*](http://lcav.epfl.ch).
+
+*FRIDA* is a new algorithm for direction of arrival (DOA) estimation
+for acoustic sources. This repository contains a python implementation
+of the algorithm, as well as five conventional methods: MUSIC, SRP-PHAT, CSSM,
+WAVES, and TOPS (in the `doa` folder).
+
+A number of scripts were written to evaluate the performance of FRIDA and the
+other algorithms in different scenarios. Monte-Carlo simulations were used to
+study the noise robustness and the minimum angle of separation for close source
+resolution (`figure_doa_separation.py`, `figure_doa_synthetic.py`).  A number
+of experiment on recorded data were done and the scripts for processing this
+data are also available (`figure_doa_experiment.py`,
+`figure_doa_9_mics_10_src.py`).
+
+We are available for any question or request relating to either the code
+or the theory behind it. Just ask!
+
+Abstract
+--------
+
+In this paper we present FRIDA --- an algorithm for estimating directions of
+arrival of multiple wideband sound sources. FRIDA combines multi-band
+information coherently and achieves state-of-the-art resolution at extremely
+low signal-to-noise ratios. It works for arbitrary array layouts, but unlike
+the various steered response power and subspace methods, it does not require a
+grid search. FRIDA leverages recent advances in sampling signals with a finite
+rate of innovation. It is based on the insight that for any array layout, the
+entries of the spatial covariance matrix can be linearly transformed into a
+uniformly sampled sum of sinusoids.
+
+Authors
+-------
+
+Hanjie Pan, Robin Scheibler, Eric Bezzam, and Martin Vetterli are with 
+Audiovisual Communications Laboratory ([LCAV](http://lcav.epfl.ch)) at 
+[EPFL](http://www.epfl.ch).
+
+Ivan Dokmanić is with Institut Langevin, CNRS, EsPCI Paris, PSL Research University.
+
+<img src="http://lcav.epfl.ch/files/content/sites/lcav/files/images/Home/LCAV_anim_200.gif">
+
+#### Contact
+
+[Robin Scheibler](mailto:robin[dot]scheibler[at]epfl[dot]ch) <br>
+EPFL-IC-LCAV <br>
+BC Building <br>
+Station 14 <br>
+1015 Lausanne
+
+Recreate the figures and sound samples
+--------------------------------------
+
+In a terminal, run the following script.
+
+    ./make_all_figures.sh
+
+Alternatively, start an ipython cluster
+
+    ipcluster start -n <number_workers>
+
+and then type in the following commands in an ipython shell.
+
+    # Simulation with different SNR values
+    %run figure_doa_synthetic.py -f <filename>
+    %run figure_doa_synthetic_plot.py -f <filename>
+
+    # Simulation of closely spaced sources
+    %run figure_doa_separation.py -f <filename>
+    %run figure_doa_separation_plot.py -f <filename>
+
+    # Experiment on speech recordings
+    %run figure_doa_experiment.py -f <filename>
+    %run figure_doa_experiment_plot.py -f <filename>
+
+    # Experiment with 10 loudspeakers and 9 microphones
+    %run figure_doa_9_mics_10_src.py -o <filename>
+    %run figure_doa_9_mics_10_src_plot.py -f <filename>
+
+The data is saved in the `data` folder and the figures generated are collected in `figures`.
+
+Data used in the paper
+----------------------
+
+The output from the simulation and processing that
+was used for the figures in the paper is stored in
+the repository in the following files.
+
+    # Simulation with different SNR values
+    data/20160911-035215_doa_synthetic.npz
+    data/20160911-161112_doa_synthetic.npz
+    data/20160911-175127_doa_synthetic.npz
+    data/20160911-192530_doa_synthetic.npz
+    data/20160911-225325_doa_synthetic.npz
+
+    # Simulation of closely spaced sources
+    data/20160910-192848_doa_separation.npz
+
+    # Experiment on speech recordings
+    data/20160909-203344_doa_experiment.npz
+
+    # Experiment with 10 loudspeakers and 9 microphones
+    data/20160913-011415_doa_9_mics_10_src.npz
+
+Recorded Data
+-------------
+
+The recorded samples are stored in the `recordings` folder.
+Detailed description and instructions are provided along the data.
+
+Overview of results
+-------------------
+
+We implemented for comparison five algorithms: incoherent MUSIC, SRP-PHAT, CSSM, WAVES, and TOPS.
+
+### Influence of Noise (Fig. 1A)
+
+We compare the robustness to noise of the different algorithms when a single source is present.
+
+<img src="https://dl.dropboxusercontent.com/u/78009186/images/FRIDA/experiment_snr_synthetic.png" height="300">
+
+### Resolving power (Fig. 1B)
+
+We study the resolution power of the different algorithms. How close can two sources become
+before the algorithm breaks down.
+
+<img src="https://dl.dropboxusercontent.com/u/78009186/images/FRIDA/experiment_minimum_separation.png" height="300">
+
+### Experiment on speech data (Fig. 2C)
+
+We record signals from 8 loudspeakers with 1, 2, or 3 sources active simultaneously. We use
+the algorithm to reconstruct the DOA and plot the statistics of the error.
+
+<img src="https://dl.dropboxusercontent.com/u/78009186/images/FRIDA/experiment_error_box.png" height="300">
+
+### Experiment with more sources than microphone (Fig. 2D)
+
+FRIDA can identifies DOA of more sources than it uses microphones. We demonstrate
+this by playing 10 loudspeakers simultaneously and recovering all DOA with only
+9 microphones.
+
+<img src="https://dl.dropboxusercontent.com/u/78009186/images/FRIDA/experiment_9_mics_10_src.png" height="400">
+
+
+Dependencies
+------------
+
+The script `system_install.sh` was used to install all the required software on a blank UBUNTU Xenial server.
+
+* A working distribution of [Python 2.7](https://www.python.org/downloads/).
+* [Numpy](http://www.numpy.org/), [Scipy](http://www.scipy.org/)
+* We use the distribution [anaconda](https://store.continuum.io/cshop/anaconda/) to simplify the setup of the environment.
+* Computations are very heavy and we use the
+  [MKL](https://store.continuum.io/cshop/mkl-optimizations/) extension of
+  Anaconda to speed things up. There is a [free license](https://store.continuum.io/cshop/academicanaconda) for academics.
+* We used ipyparallel and joblib for parallel computations.
+* [matplotlib](http://matplotlib.org) and [seaborn](https://stanford.edu/~mwaskom/software/seaborn/index.html#) for plotting the results.
+
+The pyroomacoustics is used for STFT, fractionnal delay filters, microphone arrays generation, and some more.
+
+    pip install git+https://github.com/LCAV/pyroomacoustics
+
+List of standard packages needed
+
+    numpy, scipy, pandas, ipyparallel, seaborn, zmq, joblib
+
+In addition the two following libraries are used for resample and processing of wav files
+
+    scikits.audiolab, sickits.samplerate
+
+They require install of shared libraries
+
+    # Ubuntu code
+    apt-get install libsndfile1 libsndfile1-dev libsamplerate0 libsamplerate0-dev  # Ubuntu
+
+    # OS X install
+    brew install libsndfile
+    brew install libsamplerate
+
+
+
+Systems Tested
+--------------
+
+###Linux
+
+| Machine | ICCLUSTER EPFL                  |
+|---------|---------------------------------|
+| System  | Ubuntu 16.04.5                  |
+| CPU     | Intel Xeon E5-2680 v3 (Haswell) |
+| RAM     | 64 GB                           |
+
+###OS X
+
+| Machine | MacBook Pro Retina 15-inch, Early 2013 |
+|---------|----------------------------------------|
+| System  | OS X Maverick 10.11.6                  |
+| CPU     | Intel Core i7                          |
+| RAM     | 16 GB                                  |
+
+    System Info:
+    ------------
+    Darwin 15.6.0 Darwin Kernel Version 15.6.0: Mon Aug 29 20:21:34 PDT 2016; root:xnu-3248.60.11~1/RELEASE_X86_64 x86_64
+
+    Python Info:
+    ------------
+    Python 2.7.11 :: Anaconda custom (x86_64)
+
+    Python Packages Info (conda)
+    ----------------------------
+    # packages in environment at /Users/scheibler/anaconda:
+    accelerate                2.0.2              np110py27_p0  
+    accelerate_cudalib        2.0                           0  
+    anaconda                  custom                   py27_0  
+    ipyparallel               5.0.1                    py27_0  
+    ipython                   4.2.0                    py27_0  
+    ipython-notebook          4.0.4                    py27_0  
+    ipython-qtconsole         4.0.1                    py27_0  
+    ipython_genutils          0.1.0                    py27_0  
+    joblib                    0.9.4                    py27_0  
+    mkl                       11.3.3                        0  
+    mkl-rt                    11.1                         p0  
+    mkl-service               1.1.2                    py27_2  
+    mklfft                    2.1                np110py27_p0  
+    numpy                     1.11.0                    <pip>
+    numpy                     1.11.1                   py27_0  
+    numpydoc                  0.5                       <pip>
+    pandas                    0.18.1              np111py27_0  
+    pyzmq                     15.2.0                   py27_1  
+    scikits.audiolab          0.11.0                    <pip>
+    scikits.samplerate        0.3.3                     <pip>
+    scipy                     0.17.0                    <pip>
+    scipy                     0.18.0              np111py27_0  
+    seaborn                   0.7.1                    py27_0  
+    seaborn                   0.7.1                     <pip>
+
+License
+-------
+
+Copyright (c) 2016, Hanjie Pan, Robin Scheibler, Eric Bezzam, Ivan Dokmanić, Martin Vetterli
+
+All the code in this repository is under a CC-BY-SA 4.0 license.
+
+<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-sa/4.0/80x15.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" property="dct:title">FRIDA</span> by <a xmlns:cc="http://creativecommons.org/ns#" href="http://lcav.epfl.ch" property="cc:attributionName" rel="cc:attributionURL">LCAV-EPFL</a> is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.<br />Based on a work at <a xmlns:dct="http://purl.org/dc/terms/" href="http://go.epfl.ch/FRIDA" rel="dct:source">http://go.epfl.ch/FRIDA</a>.
+

data/README.md

@@ -0,0 +1,23 @@
+Data Folder
+-----------
+
+The output from the simulation and processing that
+was used for the figures in the paper is stored in
+the repository in the following files.
+
+    # Simulation with different SNR values
+    data/20160911-035215_doa_synthetic.npz
+    data/20160911-161112_doa_synthetic.npz
+    data/20160911-175127_doa_synthetic.npz
+    data/20160911-192530_doa_synthetic.npz
+    data/20160911-225325_doa_synthetic.npz
+
+    # Simulation of closely spaced sources
+    data/20160910-192848_doa_separation.npz
+
+    # Experiment on speech recordings
+    data/20160909-203344_doa_experiment.npz
+
+    # Experiment with 10 loudspeakers and 9 microphones
+    data/20160913-011415_doa_9_mics_10_src.npz
+

doa/fri.py

@@ -37,8 +37,6 @@ def _process(self, X):
 
         # loop over all subbands
         self.num_freq = self.freq_bins.shape[0]
-        print self.freq_bins
-        print X.shape
 
         '''
         visi_noisy_all = []
@@ -71,7 +69,6 @@ def _visibilities(self, X):
             fn = self.freq_bins[band_count]
             energy = np.var(X[:,fn,:], axis=0)
             I = np.where(energy > self.noise_margin * self.noise_floor)
-            print 'Select {} frames our of {}'.format(I[0].shape[0], energy.shape[0])
             visi_noisy = extract_off_diag(cov_mtx_est(X[:,fn,I[0]]))
             visi_noisy_all.append(visi_noisy)