{"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/spectral-analysis-for-nonstationary-audio","title":"Spectral analysis for nonstationary audio","arxiv_id":"1712.10252","date":"2018-08-23","proceeding":null,"authors":[],"abstract":"A new approach for the analysis of nonstationary signals is proposed, with a\nfocus on audio applications. Following earlier contributions, nonstationarity\nis modeled via stationarity-breaking operators acting on Gaussian stationary\nrandom signals. The focus is on time warping and amplitude modulation, and an\napproximate maximum-likelihood approach based on suitable approximations in the\nwavelet transform domain is developed. This paper provides theoretical analysis\nof the approximations, and introduces JEFAS, a corresponding estimation\nalgorithm. The latter is tested and validated on synthetic as well as real\naudio signal.","url_abs":"http://arxiv.org/abs/1712.10252v3","url_pdf":"http://arxiv.org/pdf/1712.10252v3.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":"spectral-analysis-for-nonstationary-audio","repo_url":"https://github.com/AdMeynard/JEFAS","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}