{"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/our-practice-of-using-machine-learning-to","title":"Our Practice Of Using Machine Learning To Recognize Species By Voice","arxiv_id":"1810.09078","date":"2018-10-22","proceeding":null,"authors":["Siddhardha Balemarthy","Atul Sajjanhar","James Xi Zheng"],"abstract":"As the technology is advancing, audio recognition in machine learning is\nimproved as well. Research in audio recognition has traditionally focused on\nspeech. Living creatures (especially the small ones) are part of the whole\necosystem, monitoring as well as maintaining them are important tasks. Species\nsuch as animals and birds are tending to change their activities as well as\ntheir habitats due to the adverse effects on the environment or due to other\nnatural or man-made calamities. For those in far deserted areas, we will not\nhave any idea about their existence until we can continuously monitor them.\nContinuous monitoring will take a lot of hard work and labor. If there is no\ncontinuous monitoring, then there might be instances where endangered species\nmay encounter dangerous situations. The best way to monitor those species are\nthrough audio recognition. Classifying sound can be a difficult task even for\nhumans. Powerful audio signals and their processing techniques make it possible\nto detect audio of various species. There might be many ways wherein audio\nrecognition can be done. We can train machines either by pre-recorded audio\nfiles or by recording them live and detecting them. The audio of species can be\ndetected by removing all the background noise and echoes. Smallest sound is\nconsidered as a syllable. Extracting various syllables is the process we are\nfocusing on which is known as audio recognition in terms of Machine Learning\n(ML).","url_abs":"http://arxiv.org/abs/1810.09078v1","url_pdf":"http://arxiv.org/pdf/1810.09078v1.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":"our-practice-of-using-machine-learning-to","repo_url":"https://github.com/siyangBai/twittering_sparkles","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"}],"methods":[{"method_slug":"global-convolutional-network","method_name":"Global Convolutional Network"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}