/ Machine Learning

A package for audio signal processing for indoor applications

A package for audio signal processing for indoor applications

Pyroomacoustics

Pyroomacoustics is a package for audio signal processing for indoor applications. It was developed as a fast prototyping platform for beamforming algorithms in indoor scenarios.

Summary

Pyroomacoustics is a software package aimed at the rapid development
and testing of audio array processing algorithms. The content of the package
can be divided into three main components:

  1. Intuitive Python object-oriented interface to quickly construct different simulation scenarios involving multiple sound sources and microphones in 2D and 3D rooms;
  2. Fast C implementation of the image source model for general polyhedral rooms to efficiently generate room impulse responses and simulate the propagation between sources and receivers;
  3. Reference implementations of popular algorithms for STFT, beamforming, direction finding, adaptive filtering, source separation, and single channel denoising.

Together, these components form a package with the potential to speed up the time to market
of new algorithms by significantly reducing the implementation overhead in the
performance evaluation step. Please refer to this notebook <http://nbviewer.jupyter.org/github/LCAV/pyroomacoustics/blob/master/notebooks/pyroomacoustics_demo.ipynb>_
for a demonstration of the different components of this package.

Room Acoustics Simulation

Consider the following scenario.

Suppose, for example, you wanted to produce a radio crime drama, and it
so happens that, according to the scriptwriter, the story line absolutely must culminate
in a satanic mass that quickly degenerates into a violent shootout, all taking place
right around the altar of the highly reverberant acoustic environment of Oxford's
Christ Church cathedral. To ensure that it sounds authentic, you asked the Dean of
Christ Church for permission to record the final scene inside the cathedral, but
somehow he fails to be convinced of the artistic merit of your production, and declines
to give you permission. But recorded in a conventional studio, the scene sounds flat.
So what do you do?

-- Schnupp, Nelken, and King, Auditory Neuroscience, 2010

Faced with this difficult situation, pyroomacoustics can save the day by simulating
the environment of the Christ Church cathedral!

At the core of the package is a room impulse response (RIR) generator based on the
image source model that can handle

  • Convex and non-convex rooms
  • 2D/3D rooms

Both a pure Python implementation and a C accelerator are included for maximum
speed and compatibility.

The philosophy of the package is to abstract all necessary elements of
an experiment using an object-oriented programming approach. Each of these elements
is represented using a class and an experiment can be designed by combining
these elements just as one would do in a real experiment.

Let's imagine we want to simulate a delay-and-sum beamformer that uses a linear
array with four microphones in a shoe box shaped room that contains only one
source of sound. First, we create a room object, to which we add a microphone
array object, and a sound source object. Then, the room object has methods
to compute the RIR between source and receiver. The beamformer object then extends
the microphone array class and has different methods to compute the weights, for
example delay-and-sum weights. See the example below to get an idea of what the
code looks like.

The Room class also allows one to process sound samples emitted by sources,
effectively simulating the propagation of sound between sources and microphones.
At the input of the microphones composing the beamformer, an STFT (short time
Fourier transform) engine allows to quickly process the signals through the
beamformer and evaluate the output.

Reference Implementations

In addition to its core image source model simulation, pyroomacoustics
also contains a number of reference implementations of popular audio processing
algorithms for

  • Short time Fourier transform <http://pyroomacoustics.readthedocs.io/en/pypi-release/pyroomacoustics.transform.stft.html>_ (block + online)
  • beamforming <http://pyroomacoustics.readthedocs.io/en/pypi-release/pyroomacoustics.beamforming.html>_
  • direction of arrival <http://pyroomacoustics.readthedocs.io/en/pypi-release/pyroomacoustics.doa.html>_ (DOA) finding
  • adaptive filtering <http://pyroomacoustics.readthedocs.io/en/pypi-release/pyroomacoustics.adaptive.html>_ (NLMS, RLS)
  • blind source separation <http://pyroomacoustics.readthedocs.io/en/pypi-release/pyroomacoustics.bss.html>_ (AuxIVA, Trinicon, ILRMA, SparseAuxIVA)
  • single channel denoising <https://pyroomacoustics.readthedocs.io/en/pypi-release/pyroomacoustics.denoise.html>_ (Spectral Subtraction, Subspace, Iterative Wiener)

We use an object-oriented approach to abstract the details of
specific algorithms, making them easy to compare. Each algorithm can be tuned through optional parameters. We have tried to
pre-set values for the tuning parameters so that a run with the default values
will in general produce reasonable results.

Datasets

In an effort to simplify the use of datasets, we provide a few wrappers that
allow to quickly load and sort through some popular speech corpora. At the
moment we support the following.

  • CMU ARCTIC <http://www.festvox.org/cmu_arctic/>_
  • TIMIT <https://catalog.ldc.upenn.edu/ldc93s1>_
  • Google Speech Commands Dataset <https://research.googleblog.com/2017/08/launching-speech-commands-dataset.html>_

For more details, see the doc <http://pyroomacoustics.readthedocs.io/en/pypi-release/pyroomacoustics.datasets.html>_.

Quick Install

Install the package with pip::

pip install pyroomacoustics

A cookiecutter <https://github.com/fakufaku/cookiecutter-pyroomacoustics-sim>_
is available that generates a working simulation script for a few 2D/3D
scenarios::

# if necessary install cookiecutter
pip install cookiecutter

# create the simulation script
cookiecutter gh:fakufaku/cookiecutter-pyroomacoustics-sim

# run the newly created script
python <chosen_script_name>.py

Dependencies

The minimal dependencies are::

numpy 
scipy>=0.18.0
Cython

where Cython is only needed to benefit from the compiled accelerated simulator.
The simulator itself has a pure Python counterpart, so that this requirement could
be ignored, but is much slower.

On top of that, some functionalities of the package depend on extra packages::

samplerate   # for resampling signals
matplotlib   # to create graphs and plots
sounddevice  # to play sound samples
mir_eval     # to evaluate performance of source separation in examples

The requirements.txt file lists all packages necessary to run all of the
scripts in the examples folder.

This package is mainly developed under Python 3.5. We try as much as possible to keep
things compatible with Python 2.7 and run tests and builds under both. However, the tests
code coverage is far from 100% and it might happen that we break some things in Python 2.7 from
time to time. We apologize in advance for that.

Under Linux and Mac OS, the compiled accelerators require a valid compiler to
be installed, typically this is GCC. When no compiler is present, the package
will still install but default to the pure Python implementation which is much
slower. On Windows, we provide pre-compiled Python Wheels for Python 3.5 and
3.6.

Example

Here is a quick example of how to create and visual the response of a
beamformer in a room.

.. code-block:: python

import numpy as np
import matplotlib.pyplot as plt
import pyroomacoustics as pra

# Create a 4 by 6 metres shoe box room
room = pra.ShoeBox([4,6])

# Add a source somewhere in the room
room.add_source([2.5, 4.5])

# Create a linear array beamformer with 4 microphones
# with angle 0 degrees and inter mic distance 10 cm
R = pra.linear_2D_array([2, 1.5], 4, 0, 0.1)
room.add_microphone_array(pra.Beamformer(R, room.fs))

# Now compute the delay and sum weights for the beamformer
room.mic_array.rake_delay_and_sum_weights(room.sources[0][:1])

# plot the room and resulting beamformer
room.plot(freq=[1000, 2000, 4000, 8000], img_order=0)
plt.show()

More examples

A couple of detailed demos with illustrations <https://github.com/LCAV/pyroomacoustics/tree/master/notebooks>_ are available.

A comprehensive set of examples covering most of the functionalities
of the package can be found in the examples folder of the GitHub repository <https://github.com/LCAV/pyroomacoustics/tree/master/examples>_.

Authors

  • Robin Scheibler
  • Ivan Dokmanić
  • Sidney Barthe
  • Eric Bezzam
  • Hanjie Pan

How to contribute

If you would like to contribute, please clone the
repository <http://github.com/LCAV/pyroomacoustics>_ and send a pull request.

For more details, see our CONTRIBUTING <http://pyroomacoustics.readthedocs.io/en/pypi-release/contributing.html>_
page.

Academic publications

This package was developed to support academic publications. The package
contains implementations for DOA algorithms and acoustic beamformers introduced
in the following papers.

  • H. Pan, R. Scheibler, I. Dokmanic, E. Bezzam and M. Vetterli. FRIDA: FRI-based DOA estimation for arbitrary array layout, ICASSP 2017, New Orleans, USA, 2017.
  • I. Dokmanić, R. Scheibler and M. Vetterli. Raking the Cocktail Party, in IEEE Journal of Selected Topics in Signal Processing, vol. 9, num. 5, p. 825 - 836, 2015.
  • R. Scheibler, I. Dokmanić and M. Vetterli. Raking Echoes in the Time Domain, ICASSP 2015, Brisbane, Australia, 2015.

If you use this package in your own research, please cite our paper describing it <https://arxiv.org/abs/1710.04196>_.

R. Scheibler, E. Bezzam, I. Dokmanić, Pyroomacoustics: A Python package for audio room simulations and array processing algorithms, Proc. IEEE ICASSP, Calgary, CA, 2018.

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