{"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/crepe-a-convolutional-representation-for","title":"CREPE: A Convolutional Representation for Pitch Estimation","arxiv_id":"1802.06182","date":"2018-02-17","proceeding":null,"authors":["Jong Wook Kim","Justin Salamon","Peter Li","Juan Pablo Bello"],"abstract":"The task of estimating the fundamental frequency of a monophonic sound\nrecording, also known as pitch tracking, is fundamental to audio processing\nwith multiple applications in speech processing and music information\nretrieval. To date, the best performing techniques, such as the pYIN algorithm,\nare based on a combination of DSP pipelines and heuristics. While such\ntechniques perform very well on average, there remain many cases in which they\nfail to correctly estimate the pitch. In this paper, we propose a data-driven\npitch tracking algorithm, CREPE, which is based on a deep convolutional neural\nnetwork that operates directly on the time-domain waveform. We show that the\nproposed model produces state-of-the-art results, performing equally or better\nthan pYIN. Furthermore, we evaluate the model's generalizability in terms of\nnoise robustness. A pre-trained version of CREPE is made freely available as an\nopen-source Python module for easy application.","url_abs":"http://arxiv.org/abs/1802.06182v1","url_pdf":"http://arxiv.org/pdf/1802.06182v1.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":"crepe-a-convolutional-representation-for","repo_url":"https://github.com/marl/crepe","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"crepe-a-convolutional-representation-for","repo_url":"https://github.com/bpiyush/SoundOfWater","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"information-retrieval","task_name":"Information Retrieval"},{"task_slug":"music-information-retrieval","task_name":"Music Information Retrieval"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1802.06182","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1802.06182"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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