To help you get started, we've collected the most common ways that pyroomacoustics is being used within popular public projects.

LCAV / pyroomacoustics / examples / doa_algorithms.py View on Github

# compute the noise variance
sigma2 = 10**(-SNR / 10) / (4. * np.pi * distance)**2

# Create an anechoic room
room_dim = np.r_[10.,10.]
aroom = pra.ShoeBox(room_dim, fs=fs, max_order=0, sigma2_awgn=sigma2)

# add the source
source_location = room_dim / 2 + distance * np.r_[np.cos(azimuth), np.sin(azimuth)]
source_signal = np.random.randn((nfft // 2 + 1) * nfft)
aroom.add_source(source_location, signal=source_signal)

# We use a circular array with radius 15 cm # and 12 microphones
R = pra.circular_2D_array(room_dim / 2, 12, 0., 0.15)
aroom.add_microphone_array(pra.MicrophoneArray(R, fs=aroom.fs))

# run the simulation
aroom.simulate()

################################
# Compute the STFT frames needed
X = np.array([ 
    pra.stft(signal, nfft, nfft // 2, transform=np.fft.rfft).T 
    for signal in aroom.mic_array.signals ])

##############################################
# Now we can test all the algorithms available
algo_names = sorted(pra.doa.algorithms.keys())

for algo_name in algo_names:
    # Construct the new DOA object

View more ways to use pyroomacoustics

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.

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

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