{"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/hearing-anywhere-in-any-environment","title":"Hearing Anywhere in Any Environment","arxiv_id":"2504.10746","date":"2025-04-14","proceeding":"CVPR 2025 1","authors":["Xiulong Liu","Anurag Kumar","Paul Calamia","Sebastia V. Amengual","Calvin Murdock","Ishwarya Ananthabhotla","Philip Robinson","Eli Shlizerman","Vamsi Krishna Ithapu","Ruohan Gao"],"abstract":"In mixed reality applications, a realistic acoustic experience in spatial environments is as crucial as the visual experience for achieving true immersion. Despite recent advances in neural approaches for Room Impulse Response (RIR) estimation, most existing methods are limited to the single environment on which they are trained, lacking the ability to generalize to new rooms with different geometries and surface materials. We aim to develop a unified model capable of reconstructing the spatial acoustic experience of any environment with minimum additional measurements. To this end, we present xRIR, a framework for cross-room RIR prediction. The core of our generalizable approach lies in combining a geometric feature extractor, which captures spatial context from panorama depth images, with a RIR encoder that extracts detailed acoustic features from only a few reference RIR samples. To evaluate our method, we introduce ACOUSTICROOMS, a new dataset featuring high-fidelity simulation of over 300,000 RIRs from 260 rooms. Experiments show that our method strongly outperforms a series of baselines. Furthermore, we successfully perform sim-to-real transfer by evaluating our model on four real-world environments, demonstrating the generalizability of our approach and the realism of our dataset.","url_abs":"https://arxiv.org/abs/2504.10746v2","url_pdf":"https://arxiv.org/pdf/2504.10746v2.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":"abstracts"},"code_links":[{"paper_slug":"hearing-anywhere-in-any-environment","repo_url":"https://github.com/DragonLiu1995/xRIR_code","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"mixed-reality","task_name":"Mixed Reality"},{"task_slug":"room-impulse-response-rir","task_name":"Room Impulse Response (RIR)"}],"methods":[],"datasets_introduced":[{"slug":"acousticrooms","name":"AcousticRooms","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2504.10746","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2504.10746"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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