gpuRIR: A Python Library for Room Impulse Response Simulation with GPU Acceleration

TL;DR

This paper introduces gpuRIR, a Python library that leverages GPU acceleration to significantly speed up the computation of Room Impulse Responses using the Image Source Method, enabling large-scale acoustic simulations.

Contribution

The paper presents a GPU-accelerated Python library for RIR simulation that is about 100 times faster than existing CPU-based solutions, with easy usability for non-GPU programmers.

Findings

About 100 times faster than state-of-the-art CPU libraries

Uses GPU parallelization, mixed precision, and lookup tables for speed

Enables large-scale and real-time acoustic simulations

Abstract

The Image Source Method (ISM) is one of the most employed techniques to calculate acoustic Room Impulse Responses (RIRs), however, its computational complexity grows fast with the reverberation time of the room and its computation time can be prohibitive for some applications where a huge number of RIRs are needed. In this paper, we present a new implementation that dramatically improves the computation speed of the ISM by using Graphic Processing Units (GPUs) to parallelize both the simulation of multiple RIRs and the computation of the images inside each RIR. Additional speedups were achieved by exploiting the mixed precision capabilities of the newer GPUs and by using lookup tables. We provide a Python library under GNU license that can be easily used without any knowledge about GPU programming and we show that it is about 100 times faster than other state of the art CPU libraries. It may become a powerful tool for many applications that need to perform a large number of acoustic simulations, such as training machine learning systems for audio signal processing, or for real-time room acoustics simulations for immersive multimedia systems, such as augmented or virtual reality.