.. image:: https://github.com/LCAV/pyroomacoustics/raw/master/logo/pyroomacoustics_logo_horizontal.png
:alt: Pyroomacoustics logo
:align: left
------------------------------------------------------------------------------
.. image:: https://readthedocs.org/projects/pyroomacoustics/badge/?version=pypi-release
:target: http://pyroomacoustics.readthedocs.io/en/pypi-release/
:alt: Documentation Status
.. image:: https://mybinder.org/badge_logo.svg
:target: https://mybinder.org/v2/gh/LCAV/pyroomacoustics/master?filepath=notebooks%2Fpyroomacoustics_demo.ipynb
:alt: Test on mybinder
.. image:: https://img.shields.io/discord/829534160812245012?color=%237289DA&label=pyroomacoustics%20Discord&logo=discord&logoColor=white
:target: https://discord.gg/HQ3evGYk2s
:alt: Pyroomacoustics discord server
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 and ray tracing 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 <https://mybinder.org/v2/gh/LCAV/pyroomacoustics/master?filepath=notebooks%2Fpyroomacoustics_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
The core image source model and ray tracing modules are written in C++ for
better performance.
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, FastMNMF, FastMNMF2)
* `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
We have also provided a minimal `Dockerfile` example in order to install and
run the package within a Docker container. Note that you should `increase the memory <https://docs.docker.com/docker-for-mac/#resources>`_
of your containers to 4 GB. Less may also be sufficient, but this is necessary
for building the C++ code extension. You can build the container with::
docker build -t pyroom_container .
And enter the container with::
docker run -it pyroom_container:latest /bin/bash
Dependencies
------------
The minimal dependencies are::
numpy
scipy>=0.18.0
Cython
pybind11
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 3.6. The last supported version for Python 2.7 is
``0.4.3``.
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 visualize 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>`_.
A `video introduction to pyroomacoustics <https://www.youtube.com/watch?v=c3DTtc--_F4>`_.
A `video tutorial series on using pyroomacoustics <https://youtube.com/playlist?list=PL6QnpHKwdPYgxLV_Ijr6K_3Gdyfhk0SHg&si=gsSuUm9Yw2_sjYhr>`_
covering many advanced topics.
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.
License
-------
::
Copyright (c) 2014-2021 EPFL-LCAV
Permission is hereby granted, free of charge, to any person obtaining a copy of
this software and associated documentation files (the "Software"), to deal in
the Software without restriction, including without limitation the rights to
use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies
of the Software, and to permit persons to whom the Software is furnished to do
so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
Raw data
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"description": ".. image:: https://github.com/LCAV/pyroomacoustics/raw/master/logo/pyroomacoustics_logo_horizontal.png\n :alt: Pyroomacoustics logo\n :align: left\n\n------------------------------------------------------------------------------\n\n.. image:: https://readthedocs.org/projects/pyroomacoustics/badge/?version=pypi-release\n :target: http://pyroomacoustics.readthedocs.io/en/pypi-release/\n :alt: Documentation Status\n.. image:: https://mybinder.org/badge_logo.svg\n :target: https://mybinder.org/v2/gh/LCAV/pyroomacoustics/master?filepath=notebooks%2Fpyroomacoustics_demo.ipynb\n :alt: Test on mybinder\n.. image:: https://img.shields.io/discord/829534160812245012?color=%237289DA&label=pyroomacoustics%20Discord&logo=discord&logoColor=white\n :target: https://discord.gg/HQ3evGYk2s\n :alt: Pyroomacoustics discord server\n\nSummary\n-------\n\nPyroomacoustics is a software package aimed at the rapid development\nand testing of audio array processing algorithms. The content of the package\ncan be divided into three main components: \n\n1. Intuitive Python object-oriented interface to quickly construct different simulation scenarios involving multiple sound sources and microphones in 2D and 3D rooms;\n2. Fast C++ implementation of the image source model and ray tracing for general polyhedral rooms to efficiently generate room impulse responses and simulate the propagation between sources and receivers;\n3. Reference implementations of popular algorithms for STFT, beamforming, direction finding, adaptive filtering, source separation, and single channel denoising.\n\nTogether, these components form a package with the potential to speed up the time to market\nof new algorithms by significantly reducing the implementation overhead in the\nperformance evaluation step. Please refer to `this notebook <https://mybinder.org/v2/gh/LCAV/pyroomacoustics/master?filepath=notebooks%2Fpyroomacoustics_demo.ipynb>`_\nfor a demonstration of the different components of this package.\n\nRoom Acoustics Simulation\n`````````````````````````\n\nConsider the following scenario.\n\n Suppose, for example, you wanted to produce a radio crime drama, and it\n so happens that, according to the scriptwriter, the story line absolutely must culminate\n in a satanic mass that quickly degenerates into a violent shootout, all taking place\n right around the altar of the highly reverberant acoustic environment of Oxford's\n Christ Church cathedral. To ensure that it sounds authentic, you asked the Dean of\n Christ Church for permission to record the final scene inside the cathedral, but\n somehow he fails to be convinced of the artistic merit of your production, and declines\n to give you permission. But recorded in a conventional studio, the scene sounds flat.\n So what do you do?\n\n -- Schnupp, Nelken, and King, *Auditory Neuroscience*, 2010\n\nFaced with this difficult situation, **pyroomacoustics** can save the day by simulating\nthe environment of the Christ Church cathedral!\n\nAt the core of the package is a room impulse response (RIR) generator based on the\nimage source model that can handle\n\n* Convex and non-convex rooms\n* 2D/3D rooms\n\nThe core image source model and ray tracing modules are written in C++ for\nbetter performance.\n\nThe philosophy of the package is to abstract all necessary elements of\nan experiment using an object-oriented programming approach. Each of these elements\nis represented using a class and an experiment can be designed by combining\nthese elements just as one would do in a real experiment.\n\nLet's imagine we want to simulate a delay-and-sum beamformer that uses a linear\narray with four microphones in a shoe box shaped room that contains only one\nsource of sound. First, we create a room object, to which we add a microphone\narray object, and a sound source object. Then, the room object has methods\nto compute the RIR between source and receiver. The beamformer object then extends\nthe microphone array class and has different methods to compute the weights, for\nexample delay-and-sum weights. See the example below to get an idea of what the\ncode looks like.\n\nThe `Room` class also allows one to process sound samples emitted by sources,\neffectively simulating the propagation of sound between sources and microphones.\nAt the input of the microphones composing the beamformer, an STFT (short time\nFourier transform) engine allows to quickly process the signals through the\nbeamformer and evaluate the output.\n\nReference Implementations\n`````````````````````````\n\nIn addition to its core image source model simulation, **pyroomacoustics**\nalso contains a number of reference implementations of popular audio processing\nalgorithms for\n\n* `Short time Fourier transform <http://pyroomacoustics.readthedocs.io/en/pypi-release/pyroomacoustics.transform.stft.html>`_ (block + online)\n* `beamforming <http://pyroomacoustics.readthedocs.io/en/pypi-release/pyroomacoustics.beamforming.html>`_\n* `direction of arrival <http://pyroomacoustics.readthedocs.io/en/pypi-release/pyroomacoustics.doa.html>`_ (DOA) finding\n* `adaptive filtering <http://pyroomacoustics.readthedocs.io/en/pypi-release/pyroomacoustics.adaptive.html>`_ (NLMS, RLS)\n* `blind source separation <http://pyroomacoustics.readthedocs.io/en/pypi-release/pyroomacoustics.bss.html>`_ (AuxIVA, Trinicon, ILRMA, SparseAuxIVA, FastMNMF, FastMNMF2)\n* `single channel denoising <https://pyroomacoustics.readthedocs.io/en/pypi-release/pyroomacoustics.denoise.html>`_ (Spectral Subtraction, Subspace, Iterative Wiener)\n\nWe use an object-oriented approach to abstract the details of\nspecific algorithms, making them easy to compare. Each algorithm can be tuned through optional parameters. We have tried to\npre-set values for the tuning parameters so that a run with the default values\nwill in general produce reasonable results.\n\nDatasets\n````````\nIn an effort to simplify the use of datasets, we provide a few wrappers that\nallow to quickly load and sort through some popular speech corpora. At the\nmoment we support the following.\n\n* `CMU ARCTIC <http://www.festvox.org/cmu_arctic/>`_\n* `TIMIT <https://catalog.ldc.upenn.edu/ldc93s1>`_\n* `Google Speech Commands Dataset <https://research.googleblog.com/2017/08/launching-speech-commands-dataset.html>`_\n\nFor more details, see the `doc <http://pyroomacoustics.readthedocs.io/en/pypi-release/pyroomacoustics.datasets.html>`_.\n\nQuick Install\n-------------\n\nInstall the package with pip::\n\n pip install pyroomacoustics\n\nA `cookiecutter <https://github.com/fakufaku/cookiecutter-pyroomacoustics-sim>`_\nis available that generates a working simulation script for a few 2D/3D\nscenarios::\n\n # if necessary install cookiecutter\n pip install cookiecutter\n\n # create the simulation script\n cookiecutter gh:fakufaku/cookiecutter-pyroomacoustics-sim\n\n # run the newly created script\n python <chosen_script_name>.py\n\n\nWe have also provided a minimal `Dockerfile` example in order to install and\nrun the package within a Docker container. Note that you should `increase the memory <https://docs.docker.com/docker-for-mac/#resources>`_\nof your containers to 4 GB. Less may also be sufficient, but this is necessary\nfor building the C++ code extension. You can build the container with::\n\n docker build -t pyroom_container .\n\nAnd enter the container with::\n\n docker run -it pyroom_container:latest /bin/bash\n\n\nDependencies\n------------\n\nThe minimal dependencies are::\n\n numpy \n scipy>=0.18.0\n Cython\n pybind11\n\nwhere ``Cython`` is only needed to benefit from the compiled accelerated simulator.\nThe simulator itself has a pure Python counterpart, so that this requirement could\nbe ignored, but is much slower.\n\nOn top of that, some functionalities of the package depend on extra packages::\n\n samplerate # for resampling signals\n matplotlib # to create graphs and plots\n sounddevice # to play sound samples\n mir_eval # to evaluate performance of source separation in examples\n\nThe ``requirements.txt`` file lists all packages necessary to run all of the\nscripts in the ``examples`` folder.\n\nThis package is mainly developed under Python 3.6. The last supported version for Python 2.7 is\n``0.4.3``.\n\nUnder Linux and Mac OS, the compiled accelerators require a valid compiler to\nbe installed, typically this is GCC. When no compiler is present, the package\nwill still install but default to the pure Python implementation which is much\nslower. On Windows, we provide pre-compiled Python Wheels for Python 3.5 and\n3.6.\n\nExample\n-------\n\nHere is a quick example of how to create and visualize the response of a\nbeamformer in a room.\n\n.. code-block:: python\n\n import numpy as np\n import matplotlib.pyplot as plt\n import pyroomacoustics as pra\n\n # Create a 4 by 6 metres shoe box room\n room = pra.ShoeBox([4,6])\n\n # Add a source somewhere in the room\n room.add_source([2.5, 4.5])\n\n # Create a linear array beamformer with 4 microphones\n # with angle 0 degrees and inter mic distance 10 cm\n R = pra.linear_2D_array([2, 1.5], 4, 0, 0.1)\n room.add_microphone_array(pra.Beamformer(R, room.fs))\n\n # Now compute the delay and sum weights for the beamformer\n room.mic_array.rake_delay_and_sum_weights(room.sources[0][:1])\n\n # plot the room and resulting beamformer\n room.plot(freq=[1000, 2000, 4000, 8000], img_order=0)\n plt.show()\n\nMore examples\n-------------\n\nA couple of `detailed demos with illustrations <https://github.com/LCAV/pyroomacoustics/tree/master/notebooks>`_ are available. \n\nA comprehensive set of examples covering most of the functionalities\nof the package can be found in the ``examples`` folder of the `GitHub\nrepository <https://github.com/LCAV/pyroomacoustics/tree/master/examples>`_.\n\nA `video introduction to pyroomacoustics <https://www.youtube.com/watch?v=c3DTtc--_F4>`_.\n\nA `video tutorial series on using pyroomacoustics <https://youtube.com/playlist?list=PL6QnpHKwdPYgxLV_Ijr6K_3Gdyfhk0SHg&si=gsSuUm9Yw2_sjYhr>`_\ncovering many advanced topics.\n\nAuthors\n-------\n\n* Robin Scheibler\n* Ivan Dokmani\u0107\n* Sidney Barthe\n* Eric Bezzam\n* Hanjie Pan\n\nHow to contribute\n-----------------\n\nIf you would like to contribute, please clone the\n`repository <http://github.com/LCAV/pyroomacoustics>`_ and send a pull request.\n\nFor more details, see our `CONTRIBUTING\n<http://pyroomacoustics.readthedocs.io/en/pypi-release/contributing.html>`_\npage.\n\nAcademic publications\n---------------------\n\nThis package was developed to support academic publications. The package\ncontains implementations for DOA algorithms and acoustic beamformers introduced\nin the following papers.\n\n* 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.\n* I\\. Dokmani\u0107, 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.\n* R\\. Scheibler, I. Dokmani\u0107 and M. Vetterli. *Raking Echoes in the Time Domain*, ICASSP 2015, Brisbane, Australia, 2015.\n\nIf you use this package in your own research, please cite `our paper describing it <https://arxiv.org/abs/1710.04196>`_.\n\n\n R\\. Scheibler, E. Bezzam, I. Dokmani\u0107, *Pyroomacoustics: A Python package for audio room simulations and array processing algorithms*, Proc. IEEE ICASSP, Calgary, CA, 2018.\n\nLicense\n-------\n\n::\n\n Copyright (c) 2014-2021 EPFL-LCAV\n\n Permission is hereby granted, free of charge, to any person obtaining a copy of\n this software and associated documentation files (the \"Software\"), to deal in\n the Software without restriction, including without limitation the rights to\n use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies\n of the Software, and to permit persons to whom the Software is furnished to do\n so, subject to the following conditions:\n\n The above copyright notice and this permission notice shall be included in all\n copies or substantial portions of the Software.\n\n THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n SOFTWARE.\n\n",
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