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https://github.com/LCAV/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.
https://github.com/LCAV/pyroomacoustics

acoustics adaptive-filtering audio beamforming doa image-source-model room-impulse-response stft

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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.

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README 

        
.. image:: https://github.com/LCAV/pyroomacoustics/raw/master/logo/pyroomacoustics_logo_horizontal.png

   :alt: Pyroomacoustics logo

   :align: left



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    :target: http://pyroomacoustics.readthedocs.io/en/pypi-release/

    :alt: Documentation Status

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    :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

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    :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 `_

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 `_ (block + online)

* `beamforming `_

* `direction of arrival `_ (DOA) finding

* `adaptive filtering `_ (NLMS, RLS)

* `blind source separation `_ (AuxIVA, Trinicon, ILRMA, SparseAuxIVA, FastMNMF, FastMNMF2)

* `single channel denoising `_ (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 `_

* `TIMIT `_

* `Google Speech Commands Dataset `_



For more details, see the `doc `_.



Quick Install

-------------



Install the package with pip::



    pip install pyroomacoustics



A `cookiecutter `_

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 .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 `_

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 `_ 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 `_.



A `video introduction to pyroomacoustics `_.



A `video tutorial series on using pyroomacoustics `_

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 `_ and send a pull request.



For more details, see our `CONTRIBUTING

`_

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 `_.



  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.