{"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/mulan-a-blind-and-off-grid-method-for","title":"MULAN: A Blind and Off-Grid Method for Multichannel Echo Retrieval","arxiv_id":"1810.13338","date":"2018-10-31","proceeding":"NeurIPS 2018 12","authors":["Helena Peic Tukuljac","Antoine Deleforge","Rémi Gribonval"],"abstract":"This paper addresses the general problem of blind echo retrieval, i.e., given\nM sensors measuring in the discrete-time domain M mixtures of K delayed and\nattenuated copies of an unknown source signal, can the echo locations and\nweights be recovered? This problem has broad applications in fields such as\nsonars, seismol-ogy, ultrasounds or room acoustics. It belongs to the broader\nclass of blind channel identification problems, which have been intensively\nstudied in signal processing. Existing methods in the literature proceed in two\nsteps: (i) blind estimation of sparse discrete-time filters and (ii) echo\ninformation retrieval by peak-picking on filters. The precision of these\nmethods is fundamentally limited by the rate at which the signals are sampled:\nestimated echo locations are necessary on-grid, and since true locations never\nmatch the sampling grid, the weight estimation precision is impacted. This is\nthe so-called basis-mismatch problem in compressed sensing. We propose a\nradically different approach to the problem, building on the framework of\nfinite-rate-of-innovation sampling. The approach operates directly in the\nparameter-space of echo locations and weights, and enables near-exact blind and\noff-grid echo retrieval from discrete-time measurements. It is shown to\noutperform conventional methods by several orders of magnitude in precision.","url_abs":"http://arxiv.org/abs/1810.13338v1","url_pdf":"http://arxiv.org/pdf/1810.13338v1.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":"mulan-a-blind-and-off-grid-method-for","repo_url":"https://github.com/epfl-lts2/mulan","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"information-retrieval","task_name":"Information Retrieval"},{"task_slug":"retrieval","task_name":"Retrieval"},{"task_slug":"compressed-sensing","task_name":"compressed sensing"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}