Analyze, visualize and process sound field data recorded by spherical microphone arrays.
The sound_field_analysis toolbox (short: sfa) is a Python port of the Sound Field Analysis Toolbox (SOFiA) toolbox, originally by Benjamin Bernschütz . The main goal of the sfa toolbox is to analyze, visualize and process sound field data recorded by spherical microphone arrays. Furthermore, various types of test-data may be generated to evaluate the implemented functions. It is an essential building block of ReTiSAR, an implementation of real time binaural rendering of spherical microphone array data.
We use Python 3.9 for development. Chances are that earlier version will work too but this is currently untested.
The following external libraries are required:
For performance and convenience reasons we highly recommend to use Conda (miniconda for simplicity) to manage your Python installation. Once installed, you can use the following steps to receive and use sfa, depending on your use case:
From PyPI / pip:Install into an existing environment (without example Jupyter Notebooks):pip install sound_field_analysis
By cloning (or downloading) the repository and setting up a new environment:git clone https://github.com/AppliedAcousticsChalmers/sound_field_analysis-py.gitcd sound_field_analysis-py/Create a new Conda environment from the specified dependencies:conda env create --file environment.yml --forceActivate the environment:source activate sfaOptional: Install additional dependencies for development purposes (locally run Jupyter Notebooks with example, run tests, generate documentation):conda env update --file environment_dev.yml
Exp1: Ideal plane wave
Ideal unity plane wave simulation and 3D plot.
Exp2: Measured plane wave
A measured plane wave from AZ=180°, EL=90° in the anechoic chamber using a cardioid mic.
Exp4: Binaural rendering
Render a spherical microphone array impulse response measurement binaurally. The example shows examples for loading miro or SOFA files.
Implement option to use real spherical harmonic basis functions
Update Exp4 to optionally utilize real spherical harmonics
Fix testing of spherical harmonics against reference Matlab implementation
Add testing for generation of real spherical harmonics
Add evaluation of performance for generation of complex and real spherical harmonics
Add evaluation of performance for spatial sound field decomposition
Update Conda environment setup to combine all development dependencies
Update MIRO struct loading (quadrature weights are now optional)
Fix to prevent Python 3.8 syntax warnings
Improve Exp4 (general code structure and utilizing Spherical Head Filter and Spherical Harmonics Tapering)
Update README and PyPI package
Update internal documentation and string formatting
- 2019-07-30 (v0.9)
- 2019-07-11 (v0.8)
Implement Spherical Harmonics coefficients tapering
Update Spherical Head Filter to consider tapering
- 2019-06-17 (v0.7)
Implement Bandwidth Extension for Microphone Arrays (BEMA)
Edit read_miro_struct(), named tuple ArraySignal and miro_to_struct.m to load center measurements
- 2019-06-11 (v0.6)
Implement Radial Filter Improvement from Sound Field Analysis Toolbox (SOFiA) toolbox
- 2019-05-23 (v0.5)
Implement Spherical Head Filter
Implement Spherical Fourier Transform using pseudo-inverse
Extract real time capable spatial Fourier transform
Extract reversed m index function (Update Exp4)
See CONTRIBUTE.rst for full details.
You can find the full offline documentation as PDF as well as online at https://appliedacousticschalmers.github.io/sound_field_analysis-py/ .
This software is licensed under the MIT License (see LICENSE for full details).
The sound_field_analysis toolbox is based on the Matlab/C++ Sound Field Analysis Toolbox (SOFiA) toolbox by Benjamin Bernschütz. For more information you may refer to the original publication:
The Lebedev grid generation was adapted from an implementation by Richard P. Muller.
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