{"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/the-orchive-data-mining-a-massive-bioacoustic","title":"The Orchive : Data mining a massive bioacoustic archive","arxiv_id":"1307.0589","date":"2013-07-02","proceeding":null,"authors":["Steven Ness","Helena Symonds","Paul Spong","George Tzanetakis"],"abstract":"The Orchive is a large collection of over 20,000 hours of audio recordings\nfrom the OrcaLab research facility located off the northern tip of Vancouver\nIsland. It contains recorded orca vocalizations from the 1980 to the present\ntime and is one of the largest resources of bioacoustic data in the world. We\nhave developed a web-based interface that allows researchers to listen to these\nrecordings, view waveform and spectral representations of the audio, label\nclips with annotations, and view the results of machine learning classifiers\nbased on automatic audio features extraction. In this paper we describe such\nclassifiers that discriminate between background noise, orca calls, and the\nvoice notes that are present in most of the tapes. Furthermore we show\nclassification results for individual calls based on a previously existing orca\ncall catalog. We have also experimentally investigated the scalability of\nclassifiers over the entire Orchive.","url_abs":"http://arxiv.org/abs/1307.0589v1","url_pdf":"http://arxiv.org/pdf/1307.0589v1.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":"the-orchive-data-mining-a-massive-bioacoustic","repo_url":"https://github.com/sness/orchive","is_official":0,"mentioned_in_paper":0,"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}