Metadata-Version: 2.1
Name: pyroomacoustics
Version: 0.6.0
Summary: A simple framework for room acoustics and audio processing in Python.
Home-page: https://github.com/LCAV/pyroomacoustics
Author: Laboratory for Audiovisual Communications, EPFL
Author-email: fakufaku@gmail.ch
License: MIT
Description: .. image:: https://github.com/LCAV/pyroomacoustics/raw/master/logo/pyroomacoustics_logo_horizontal.png
           :scale: 80 %
           :alt: Pyroomacoustics logo
           :align: left
        
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        .. image:: https://travis-ci.org/LCAV/pyroomacoustics.svg?branch=pypi-release
            :target: https://travis-ci.org/LCAV/pyroomacoustics
        .. 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)
        * `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>`_.
        
        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.
        
        
Keywords: room acoustics signal processing doa beamforming adaptive
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Information Technology
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Multimedia :: Sound/Audio :: Speech
Classifier: Topic :: Multimedia :: Sound/Audio :: Analysis
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/x-rst
