Sat2Sound: A Unified Framework for Zero-Shot Soundscape Mapping

[2505.13777] Sat2Sound: A Unified Framework for Zero-Shot Soundscape Mapping Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computer Vision and Pattern Recognition arXiv:2505.13777 (cs) [Submitted on 19 May 2025 ( v1 ), last revised 11 Apr 2026 (this version, v2)] Title: Sat2Sound: A Unified Framework for Zero-Shot Soundscape Mapping Authors: Subash Khanal , Srikumar Sastry , Aayush Dhakal , Adeel Ahmad , Abby Stylianou , Nathan Jacobs View a PDF of the paper titled Sat2Sound: A Unified Framework for Zero-Shot Soundscape Mapping, by Subash Khanal and 5 other authors View PDF HTML (experimental) Abstract: We present Sat2Sound, a unified multimodal framework for geospatial soundscape understanding, designed to predict and map the distribution of sounds across the Earth's surface. Existing methods for this task rely on paired satellite images and geotagged audio samples, which often fail to capture the full diversity of sound at a location. Sat2Sound overcomes this limitation by augmenting datasets with semantically rich, vision-language model-generated soundscape descriptions, which broaden the range of possible ambient sounds represented at each location. Our framework jointly learns from audio, text descriptions of audio, satellite images, and synthetic image captions through contrastive and codebook-aligned learning, discovering a set of "soundscape concepts" shared across modalities, enabling hyper-localized, explainable soundscape mapping. Sat2Sound achieves state-of-the-art performance in cross-modal retrieval between satellite image and audio on the GeoSound and SoundingEarth benchmarks. Finally, by retrieving detailed soundscape captions that can be rendered through text-to-audio models, Sat2Sound enables location-conditioned soundscape synthesis for immersive and educational applications, even with limited computational resources. Our code and models are available at this https URL . Comments: Accepted to EarthVision 2026 Subjects: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI); Sound (cs.SD) Cite as: arXiv:2505.13777 [cs.CV] (or arXiv:2505.13777v2 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2505.13777 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Subash Khanal [ view email ] [v1] Mon, 19 May 2025 23:36:04 UTC (3,625 KB) [v2] Sat, 11 Apr 2026 05:01:03 UTC (7,298 KB) Full-text links: Access Paper: View a PDF of the paper titled Sat2Sound: A Unified Framework for Zero-Shot Soundscape Mapping, by Subash Khanal and 5 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV < prev | next > new | recent | 2025-05 Change to browse by: cs cs.AI cs.SD References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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