{"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/a-comparison-of-audio-signal-preprocessing","title":"A Comparison of Audio Signal Preprocessing Methods for Deep Neural Networks on Music Tagging","arxiv_id":"1709.01922","date":"2017-09-06","proceeding":null,"authors":["Keunwoo Choi","György Fazekas","Kyunghyun Cho","Mark Sandler"],"abstract":"In this paper, we empirically investigate the effect of audio preprocessing on music tagging with deep neural networks. We perform comprehensive experiments involving audio preprocessing using different time-frequency representations, logarithmic magnitude compression, frequency weighting, and scaling. We show that many commonly used input preprocessing techniques are redundant except magnitude compression.","url_abs":"https://arxiv.org/abs/1709.01922v3","url_pdf":"https://arxiv.org/pdf/1709.01922v3.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":"a-comparison-of-audio-signal-preprocessing","repo_url":"https://github.com/GorillaBus/urban-audio-classifier","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"LGPL-3.0"}}],"tasks":[{"task_slug":"music-tagging","task_name":"Music Tagging"}],"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}