{"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/neural-audio-effect-modelling-strategies-for","title":"Neural Audio Effect Modelling Strategies for a Spring Reverb","arxiv_id":null,"date":"2023-09-14","proceeding":"Zenodo 2023 9","authors":["Xavier Lizarraga"],"abstract":"Virtual analog modelling emulates the processing characteristics of a given physical device. This has been an active field of research and commercial innovation in which two main perspectives have been historically adopted. The first one: white-box, seeks to reproduce the exact behaviour through algorithmic simulation of circuits or physical phenomena. The second one: black-box,\r\naims to learn the approximation function from examples recorded at the input and output stages of the target device. In this second approach, deep learning has emerged as a valuable strategy for linear systems, such as filters, as well as nonlinear time-dependent ones like distortion circuits or compressors.\r\nThe spring reverb is as audio effect with a very long and rooted history in music production and performance, based on a relatively simple design, this device is an effective tool for artificial reverberation. The electromechanical functioning of this reverb makes it a nonlinear timeinvariant spatial system that is difficult to fully emulate in the digital domain with white-box modelling techniques.\r\nThis thesis wants to address the modelling of spring reverb, leveraging end-to-end neural audio effect architectures through supervised learning. Recurrent, convolutional and hybrid models have successfully been used for similar tasks, especially compressors and distortion circuits emulations. Using two available datasets of guitar recordings, we evaluate with quantitative\r\nmetrics, acoustical analysis and signal processing measurements the efficiency and the results of four neural network architectures to model this effect. We present the results and outline different strategies for this modelling task, providing a reproducible experimental environment with code.","url_abs":"https://zenodo.org/records/8380480","url_pdf":"https://zenodo.org/records/8380480/files/Francesco-Papaleo-Master-Thesis-2023.pdf?download=1","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":"neural-audio-effect-modelling-strategies-for","repo_url":"https://github.com/francescopapaleo/neural-audio-spring-reverb","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","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}