{"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/recurrent-neural-networks-for-polyphonic","title":"Recurrent Neural Networks for Polyphonic Sound Event Detection in Real Life Recordings","arxiv_id":"1604.00861","date":"2016-04-04","proceeding":null,"authors":["Giambattista Parascandolo","Heikki Huttunen","Tuomas Virtanen"],"abstract":"In this paper we present an approach to polyphonic sound event detection in\nreal life recordings based on bi-directional long short term memory (BLSTM)\nrecurrent neural networks (RNNs). A single multilabel BLSTM RNN is trained to\nmap acoustic features of a mixture signal consisting of sounds from multiple\nclasses, to binary activity indicators of each event class. Our method is\ntested on a large database of real-life recordings, with 61 classes (e.g.\nmusic, car, speech) from 10 different everyday contexts. The proposed method\noutperforms previous approaches by a large margin, and the results are further\nimproved using data augmentation techniques. Overall, our system reports an\naverage F1-score of 65.5% on 1 second blocks and 64.7% on single frames, a\nrelative improvement over previous state-of-the-art approach of 6.8% and 15.1%\nrespectively.","url_abs":"http://arxiv.org/abs/1604.00861v1","url_pdf":"http://arxiv.org/pdf/1604.00861v1.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":"recurrent-neural-networks-for-polyphonic","repo_url":"https://github.com/yardencsGitHub/tf_syllable_segmentation_annotation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"recurrent-neural-networks-for-polyphonic","repo_url":"https://github.com/yardencsGitHub/tweetynet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"event-detection","task_name":"Event Detection"},{"task_slug":"sound-event-detection","task_name":"Sound Event Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1604.00861","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}