{"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/let-the-paintings-play","title":"Let the paintings play","arxiv_id":"2206.14142","date":"2022-05-26","proceeding":null,"authors":["Paola Gervasio","Alfio Quarteroni","Daniele Cassani"],"abstract":"In this paper, we introduce a mathematical method to extract similarities between paintings and musical tracks. Our approach is based on the digitalization of both paintings and musical tracks by means of finite expansions in terms of orthogonal basis functions (with both Fourier and wavelet bases). The best fit between a specific painting and a sample of musical tracks from a given composer is achieved via an $L^2$ projection upon a finite-dimensional subspace. Several examples are provided for the analysis of a collection of works of art by the Italian artist Marcello Morandini. Finally, we have developed an original applet that implements the process above and which can be freely downloaded from the site https://github.com/pgerva/playing-paintings.git","url_abs":"https://arxiv.org/abs/2206.14142v3","url_pdf":"https://arxiv.org/pdf/2206.14142v3.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":"let-the-paintings-play","repo_url":"https://github.com/pgerva/playing-paintings","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}