{"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/investigating-label-noise-sensitivity-of","title":"Investigating Label Noise Sensitivity of Convolutional Neural Networks for Fine Grained Audio Signal Labelling","arxiv_id":"1805.10880","date":"2018-05-28","proceeding":null,"authors":["Rainer Kelz","Gerhard Widmer"],"abstract":"We measure the effect of small amounts of systematic and random label noise\ncaused by slightly misaligned ground truth labels in a fine grained audio\nsignal labeling task. The task we choose to demonstrate these effects on is\nalso known as framewise polyphonic transcription or note quantized multi-f0\nestimation, and transforms a monaural audio signal into a sequence of note\nindicator labels. It will be shown that even slight misalignments have clearly\napparent effects, demonstrating a great sensitivity of convolutional neural\nnetworks to label noise. The implications are clear: when using convolutional\nneural networks for fine grained audio signal labeling tasks, great care has to\nbe taken to ensure that the annotations have precise timing, and are free from\nsystematic or random error as much as possible - even small misalignments will\nhave a noticeable impact.","url_abs":"http://arxiv.org/abs/1805.10880v1","url_pdf":"http://arxiv.org/pdf/1805.10880v1.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":"investigating-label-noise-sensitivity-of","repo_url":"https://github.com/rainerkelz/ICASSP18","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"sensitivity","task_name":"Sensitivity"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}