{"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/sound-event-detection-in-domestic","title":"Sound event detection in domestic environments withweakly labeled data and soundscape synthesis","arxiv_id":null,"date":"2019-10-26","proceeding":null,"authors":["Nicolas Turpault","Romain Serizel","Ankit Shah","Justin Salamon"],"abstract":"This paper presents Task 4 of the Detection and Classification of Acoustic  Scenes  and  Events  (DCASE)  2019  challenge  and  provides a first analysis of the challenge results.  The task is a follow-up  to  Task  4  of  DCASE  2018,  and  involves  training  systems for  large-scale  detection  of  sound  events  using  a  combination  of weakly  labeled  data,  i.e.  training  labels  without  time  boundaries,and strongly-labeled synthesized data.   The paper introduces Domestic Environment Sound Event Detection (DESED) dataset mixing a part of last year dataset and an additional synthetic, strongly labeled, dataset provided this year that we’ll describe more in de-tail.  We also report the performance of the submitted systems on the official evaluation (test) and development sets as well as several additional datasets. The best systems from this year outperform last year’s winning system by about 10% points in terms of F-measure.","url_abs":"https://hal.inria.fr/hal-02160855","url_pdf":"https://hal.inria.fr/hal-02355573v2/document","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":"sound-event-detection-in-domestic","repo_url":"https://github.com/turpaultn/DCASE2019_task4","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"event-detection","task_name":"Event Detection"},{"task_slug":"sound-event-detection","task_name":"Sound Event Detection"}],"methods":[],"datasets_introduced":[{"slug":"desed","name":"DESED","full_name":"Domestic environment sound event detection"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/sound-event-detection-on-desed","task":"Sound Event Detection","dataset":"DESED","model":"Baseline","rank_in_archive_order":9,"of":13,"metrics":{"event-based F1 score":"25.8"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}