{"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/now-playing-continuous-low-power-music","title":"Now Playing: Continuous low-power music recognition","arxiv_id":"1711.10958","date":"2017-11-29","proceeding":null,"authors":["Blaise Agüera y Arcas","Beat Gfeller","Ruiqi Guo","Kevin Kilgour","Sanjiv Kumar","James Lyon","Julian Odell","Marvin Ritter","Dominik Roblek","Matthew Sharifi","Mihajlo Velimirović"],"abstract":"Existing music recognition applications require a connection to a server that\nperforms the actual recognition. In this paper we present a low-power music\nrecognizer that runs entirely on a mobile device and automatically recognizes\nmusic without user interaction. To reduce battery consumption, a small music\ndetector runs continuously on the mobile device's DSP chip and wakes up the\nmain application processor only when it is confident that music is present.\nOnce woken, the recognizer on the application processor is provided with a few\nseconds of audio which is fingerprinted and compared to the stored fingerprints\nin the on-device fingerprint database of tens of thousands of songs. Our\npresented system, Now Playing, has a daily battery usage of less than 1% on\naverage, respects user privacy by running entirely on-device and can passively\nrecognize a wide range of music.","url_abs":"http://arxiv.org/abs/1711.10958v1","url_pdf":"http://arxiv.org/pdf/1711.10958v1.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":"now-playing-continuous-low-power-music","repo_url":"https://github.com/magcil/deep-audio-fingerprinting-benchmark","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","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}