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        "author": "Laboratory for Audiovisual Communications, EPFL",
        "author_email": "fakufaku@gmail.ch",
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        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Information Technology",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.5",
            "Topic :: Multimedia :: Sound/Audio :: Analysis",
            "Topic :: Multimedia :: Sound/Audio :: Speech",
            "Topic :: Scientific/Engineering :: Information Analysis",
            "Topic :: Scientific/Engineering :: Physics"
        ],
        "description": ".. image:: https://github.com/LCAV/pyroomacoustics/raw/master/logo/pyroomacoustics_logo_horizontal.png\n   :scale: 80 %\n   :alt: Pyroomacoustics logo\n   :align: left\n\n------------------------------------------------------------------------------\n\n.. image:: https://travis-ci.org/LCAV/pyroomacoustics.svg?branch=pypi-release\n    :target: https://travis-ci.org/LCAV/pyroomacoustics\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\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 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 <http://nbviewer.jupyter.org/github/LCAV/pyroomacoustics/blob/master/notebooks/pyroomacoustics_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\nBoth a pure Python implementation and a C accelerator are included for maximum\nspeed and compatibility.\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)\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\nDependencies\n------------\n\nThe minimal dependencies are::\n\n    numpy \n    scipy>=0.18.0\n    Cython\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 3.5. We try as much as possible to keep\nthings compatible with Python 2.7 and run tests and builds under both. However, the tests\ncode coverage is far from 100% and it might happen that we break some things in Python 2.7 from\ntime to time. We apologize in advance for that.\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 visual 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\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) 2014-2018 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.",
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