DavidDiazGuerra/gpuRIR
Python library for Room Impulse Response (RIR) simulation with GPU acceleration
What it solves
It provides a high-performance way to simulate Room Impulse Responses (RIRs), which are essential for understanding how sound behaves in a specific physical space. Traditional CPU-based simulations are often slow; this library uses GPU acceleration to compute these responses significantly faster.
How it works
The library implements the Image Source Method (ISM) using CUDA GPUs to calculate the RIRs between multiple source and receiver positions in parallel. It includes tools to estimate reflection coefficients based on the Sabine model and can handle various microphone and speaker polar patterns (e.g., cardioid, bidirectional).
Who it’s for
Researchers and engineers working in acoustics, audio signal processing, and spatial audio who need to generate synthetic room acoustics data quickly.
Highlights
- GPU Acceleration: Approximately 100 times faster than CPU implementations.
- Parallel Computation: Computes RIRs for multiple sources and receivers simultaneously.
- Mixed Precision: Supports mixed precision mode for Pascal GPU architecture and newer.
- Trajectory Simulation: Ability to filter audio signals based on a motion trajectory recorded by a microphone array.
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