{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/gsound-sir-a-spatial-impulse-response-ray","title":"GSound-SIR: A Spatial Impulse Response Ray-Tracing and High-order Ambisonic Auralization Python Toolkit","arxiv_id":"2503.17866","date":"2025-03-22","proceeding":null,"authors":["Yongyi Zang","Qiuqiang Kong"],"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.","url_abs":"https://arxiv.org/abs/2503.17866v1","url_pdf":"https://arxiv.org/pdf/2503.17866v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"gsound-sir-a-spatial-impulse-response-ray","repo_url":"https://github.com/yongyizang/gsound-sir","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}