gpuRIR is a free and open-source Python library for Room Impulse Response (RIR) simulation using the Image Source Method (ISM) with GPU acceleration. It can compute the RIRs between several source and receivers positions in parallel using CUDA GPUs. It is approximately 100 times faster than CPU implementations [1].
OS: It has been tested on GNU/Linux systems (Ubuntu and centOS) and Windows 10. Please, let me know if you successfully install it on Mac OSX systems.
Compilers: To install the package you will need the NVIDIA CUDA Toolkit (it has been tested with the release 8.0 and 10.0 but it should work fine with any version that includes cuRAND) and a C++11 compiler, such as GCC or MSVC++.
CMake: Finally, you will need, at least, the version 3.23 of CMake. You can easily get it by pip install cmake.
Python: It has been tested in Python 3, but should work fine with Python 2.
Note for PyTorch users: If you are going to use this module with PyTorch, the compiler you use to build gpuRIR must be ABI-compatible with the compiler PyTorch was built with, so you must use GCC version 4.9 and above.
You can use pip to install gpuRIR from our repository through pip install https://github.com/DavidDiazGuerra/gpuRIR/zipball/master. You can also clone or download our repository and run pip install gpuRIR/.
The library is subject to AGPL-3.0 license and comes with no warranty. If you find it useful for your research work, please, acknowledge it to [1].
simulateRIRRoom Impulse Responses (RIRs) simulation using the Image Source Method (ISM). For further details see [1].
3D ndarray The first axis is the source, the second the receiver and the third the time.
Asking for too much and too long RIRs (specially for full ISM simulations) may exceed the GPU memory and crash the kernel.
simulateTrajectoryFilter an audio signal by the RIRs of a motion trajectory recorded with a microphone array.
2D ndarray Matrix with the signals captured by each microphone in each column.
activateMixedPrecisionActivate the mixed precision mode, only for Pascal GPU architecture or superior.
activateLUTActivate the lookup table for the sinc computations.
beta_SabineEstimationEstimation of the reflection coefficients needed to have the desired reverberation time.
ndarray with 6 elements. Reflection coefficients of the walls as $[\beta{x0}, \beta{x1}, \beta{y0}, \beta{y1}, \beta{z0}, \beta{z1}]$, where $\beta{x0}$ and $\beta{x1}$ are the reflection coefficients of the walls orthogonal to the x axis at x=0 and x=room_sz[0], respectively.
att2t_SabineEstimatorEstimation of the time for the RIR to reach a certain attenuation using the Sabine model.
float. Time (in seconds) to reach the desired attenuation.
t2nEstimation of the number of images needed for a correct RIR simulation.
3 elements list of integers. The number of images sources to compute in each dimension.
[1] Diaz-Guerra, D., Miguel, A. & Beltran, J.R. gpuRIR: A python library for room impulse response simulation with GPU acceleration. Multimed Tools Appl (2020). [DOI] [SharedIt] [arXiv preprint]