GSound-SIR: A Spatial Impulse Response Ray-Tracing and High-order Ambisonic Auralization Python Toolkit (2503.17866v1)

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 Significant Tool for Room Acoustics Simulation

The paper "GSound-SIR: A Spatial Impulse Response Ray-Tracing and High-order Ambisonic Auralization Python Toolkit" presents a sophisticated Python-based toolkit for simulating room acoustics, specifically designed to address limitations in existing ray-tracing applications. Traditional tools often operate as opaque systems, limiting access to raw simulation data and complicating the integration of intermediate results into custom workflows. In response, GSound-SIR provides an open framework enabling users to access detailed path data, enhancing both the fidelity and flexibility of spatial audio simulations.

Key Contributions

1. Access to Raw Ray Data: Unlike previous tools that largely encapsulate the simulation process, GSound-SIR grants direct access to extensive sets of raw ray data points. This capability facilitates a more detailed examination of sound propagation paths, allowing researchers to dissect and analyze acoustic behavior with unprecedented granularity.
2. High-Order Ambisonic Synthesis: The toolkit supports the conversion of acoustic rays into high-order Ambisonic impulse responses. By utilizing higher-order Ambisonics, GSound-SIR captures spatial audio cues with more precision, benefiting applications in areas such as virtual reality and advanced gaming environments.
3. Efficient Data Handling: The inclusion of an energy-based filtering algorithm ensures that only the most critical ray data is retained, significantly reducing storage requirements without compromising the accuracy of the reconstructed energy profile. Data is stored in Parquet format, optimizing both I/O performance and compatibility with data analysis pipelines.
4. Python Integration: Through a Python interface enabled by pybind11, the toolkit allows seamless integration with Python-based data science workflows, permitting flexible manipulation of raw ray data prior to auralization. Auralization itself is performed up to the ninth order using efficient, recurrence-relation-based methods, supporting high-performance computation while allowing customization of rendering processes.

Experimental Evaluation

The authors conducted an extensive series of benchmarks to evaluate the performance of GSound-SIR under realistic conditions. Results demonstrated:

• An inverse relationship between room size and computation time, explained by the reduced likelihood of valid ray paths in larger spaces.
• Linear scaling of computation time with the number of rays, confirming the system's efficient handling of large datasets.
• Substantial acoustic energy being captured by a small subset of rays, pointing to potential optimization by focusing computation on high-energy paths.
• Linear growth of disk write times and file sizes relative to the percentage of rays stored, validating the proposed selective storage approach.

Implementation and Future Directions

Leveraging the GSound ray-tracing engine, the proposed toolkit enhances the flexibility and efficiency of acoustic simulations in stationary environments. While the current version does not support dynamic sources or GPU acceleration, these limitations present clear avenues for future improvement. Future work could involve extending the auralization capabilities to unify different SIR rendering methodologies and employing deep learning-based spatial upsampling techniques, potentially increasing both efficiency and fidelity.

In conclusion, GSound-SIR emerges as a significant advancement in the field of spatial audio research, providing a comprehensive platform for both the development and evaluation of sophisticated room acoustics models. With its open-source release, this toolkit promises to be an indispensable resource for researchers seeking greater control and insight into the complex interactions governing sound propagation in immersive environments.