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GSound-SIR: A Spatial Impulse Response Ray-Tracing and High-order Ambisonic Auralization Python Toolkit

Yongyi Zang, Qiuqiang Kong

TL;DR

GSound-SIR tackles the problem of opaque, end-to-end spatial-audio pipelines by decoupling ray generation from HOA-based auralization and exposing intermediate ray data for analysis. The approach provides a Python-based toolkit with a standalone HOA renderer, energy-based ray filtering, and Parquet storage, enabling large-scale, flexible experiments. Key contributions include direct access to millions of ray paths, a nine-order HOA auralization module, and efficient data handling that preserves acoustic fidelity while reducing storage needs. This work facilitates detailed propagation analysis, reproducible workflows, and easier integration with modern data pipelines for advanced spatial audio research.

Abstract

Accurate and efficient simulation of room impulse responses is crucial for spatial audio applications. However, existing acoustic ray-tracing tools often operate as black boxes and only output impulse responses (IRs), providing limited access to intermediate data or spatial fidelity. To address those problems, this paper presents GSound-SIR, a novel Python-based toolkit for room acoustics simulation that addresses these limitations. The contribution of this paper includes the follows. First, GSound-SIR provides direct access to up to millions of raw ray data points from simulations, enabling in-depth analysis of sound propagation paths that was not possible with previous solutions. Second, we introduce a tool to convert acoustic rays into high-order Ambisonic impulse response synthesis, capturing spatial audio cues with greater fidelity than standard techniques. Third, to enhance efficiency, the toolkit implements an energy-based filtering algorithm and can export only the top-X or top-X-% rays. Fourth, we propose to store the simulation results into Parquet formats, facilitating fast data I/O and seamless integration with data analysis workflows. Together, these features make GSound-SIR an advanced, efficient, and modern foundation for room acoustics research, providing researchers and developers with a powerful new tool for spatial audio exploration. We release the library under Apache 2.0 License at https://github.com/yongyizang/GSound-SIR.

GSound-SIR: A Spatial Impulse Response Ray-Tracing and High-order Ambisonic Auralization Python Toolkit

TL;DR

GSound-SIR tackles the problem of opaque, end-to-end spatial-audio pipelines by decoupling ray generation from HOA-based auralization and exposing intermediate ray data for analysis. The approach provides a Python-based toolkit with a standalone HOA renderer, energy-based ray filtering, and Parquet storage, enabling large-scale, flexible experiments. Key contributions include direct access to millions of ray paths, a nine-order HOA auralization module, and efficient data handling that preserves acoustic fidelity while reducing storage needs. This work facilitates detailed propagation analysis, reproducible workflows, and easier integration with modern data pipelines for advanced spatial audio research.

Abstract

Accurate and efficient simulation of room impulse responses is crucial for spatial audio applications. However, existing acoustic ray-tracing tools often operate as black boxes and only output impulse responses (IRs), providing limited access to intermediate data or spatial fidelity. To address those problems, this paper presents GSound-SIR, a novel Python-based toolkit for room acoustics simulation that addresses these limitations. The contribution of this paper includes the follows. First, GSound-SIR provides direct access to up to millions of raw ray data points from simulations, enabling in-depth analysis of sound propagation paths that was not possible with previous solutions. Second, we introduce a tool to convert acoustic rays into high-order Ambisonic impulse response synthesis, capturing spatial audio cues with greater fidelity than standard techniques. Third, to enhance efficiency, the toolkit implements an energy-based filtering algorithm and can export only the top-X or top-X-% rays. Fourth, we propose to store the simulation results into Parquet formats, facilitating fast data I/O and seamless integration with data analysis workflows. Together, these features make GSound-SIR an advanced, efficient, and modern foundation for room acoustics research, providing researchers and developers with a powerful new tool for spatial audio exploration. We release the library under Apache 2.0 License at https://github.com/yongyizang/GSound-SIR.

Paper Structure

This paper contains 18 sections, 4 figures.

Table of Contents

  1. Introduction
  2. Related Work
  3. Numeric Methods
  4. Geometric Methods
  5. Spatial Audio Representation
  6. Implementations
  7. Ray casting
  8. Path validation
  9. Calculate path properties
  10. Python Interface
  11. High-order Ambisonic (HOA) auralization
  12. Experiments
  13. Room Size
  14. Ray Count
  15. Energy Distribution
  16. ...and 3 more sections

Figures (4)

  • Figure 1: Computation time under various room sizes. Scale factor goes from 0.1 to 10, indicating room sizes from 1 m $\times$ 0.6 m $\times$ 0.4 m to 100 m $\times$ 60 m $\times$ 40 m
  • Figure 2: Performance analysis showing the relationships between computation time, total ray count, and successfully identified valid paths. Results are grouped by specular-to-diffuse ray ratios.
  • Figure 3: Cumulative acoustic energy distribution showing the total energy captured as a function of the percentage of highest-energy rays included.
  • Figure 4: Storage performance metrics showing the relationship between disk write time and resulting file size as a function of the percentage of highest-energy rays stored.