{"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/musical-tempo-and-key-estimation-using","title":"Musical Tempo and Key Estimation using Convolutional Neural Networks with Directional Filters","arxiv_id":"1903.10839","date":"2019-03-26","proceeding":null,"authors":["Hendrik Schreiber","Meinard Müller"],"abstract":"In this article we explore how the different semantics of spectrograms' time\nand frequency axes can be exploited for musical tempo and key estimation using\nConvolutional Neural Networks (CNN). By addressing both tasks with the same\nnetwork architectures ranging from shallow, domain-specific approaches to deep\nvariants with directional filters, we show that axis-aligned architectures\nperform similarly well as common VGG-style networks developed for computer\nvision, while being less vulnerable to confounding factors and requiring fewer\nmodel parameters.","url_abs":"http://arxiv.org/abs/1903.10839v1","url_pdf":"http://arxiv.org/pdf/1903.10839v1.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":"musical-tempo-and-key-estimation-using","repo_url":"https://github.com/hendriks73/directional_cnns","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[],"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}