{"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/utilizing-domain-knowledge-in-end-to-end","title":"Utilizing Domain Knowledge in End-to-End Audio Processing","arxiv_id":"1712.00254","date":"2017-12-01","proceeding":null,"authors":["Tycho Max Sylvester Tax","Jose Luis Diez Antich","Hendrik Purwins","Lars Maaløe"],"abstract":"End-to-end neural network based approaches to audio modelling are generally\noutperformed by models trained on high-level data representations. In this\npaper we present preliminary work that shows the feasibility of training the\nfirst layers of a deep convolutional neural network (CNN) model to learn the\ncommonly-used log-scaled mel-spectrogram transformation. Secondly, we\ndemonstrate that upon initializing the first layers of an end-to-end CNN\nclassifier with the learned transformation, convergence and performance on the\nESC-50 environmental sound classification dataset are similar to a CNN-based\nmodel trained on the highly pre-processed log-scaled mel-spectrogram features.","url_abs":"http://arxiv.org/abs/1712.00254v1","url_pdf":"http://arxiv.org/pdf/1712.00254v1.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":"utilizing-domain-knowledge-in-end-to-end","repo_url":"https://github.com/corticph/MSTmodel","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"environmental-sound-classification","task_name":"Environmental Sound Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"sound-classification","task_name":"Sound Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}