{"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/introducing-eg-ipt-and-ipt-a-novel-electric","title":"Introducing EG-IPT and ipt~: a novel electric guitar dataset and a new Max/MSP object for real-time classification of instrumental playing techniques","arxiv_id":null,"date":"2025-06-24","proceeding":"NIME 2025 6","authors":["Marco Fiorini","Nicolas Brochec","Joakim Borg","Riccardo Pasini"],"abstract":"This paper presents two key contributions to the real-time classification of Instrumental Playing Techniques (IPTs) in the context of NIME and human-machine interactive systems: the EG-IPT dataset and the ipt∼ Max/MSP object. The EG-IPT dataset, specifically designed for electric guitar, encompasses a broad range of IPTs captured across six distinct audio sources (five microphones and one direct input) and three pickup configurations. This diversity in recording conditions provides a robust foundation for training accurate models. We evaluate the dataset by employing a Convolutional Neural Network-based classifier (CNN), achieving state-of-the-art performance across a wide array of IPT classes, thereby validating the dataset's efficacy. The ipt∼ object is a new Max/MSP external enabling real-time classification of IPTs via pre-trained CNN models. While in this paper it's demonstrated with the EG-IPT dataset, the ipt∼ object is adaptable to models trained on various instruments. By integrating EG-IPT and ipt∼, we introduce a novel, end-to-end workflow that spans from data collection, model training to real-time classification and humancomputer interaction. This workflow exemplifies the entanglement of diverse components (data acquisition, machine learning, real-time processing, and interactive control) within a unified system, advancing the potential for dynamic, real-time music performance and human-computer interaction in the context of NIME.","url_abs":"https://hal.science/hal-05061680/","url_pdf":"https://hal.science/hal-05061680v1/file/NIME2025___Introducing_EG_IPT_and_ipt_Camera_Ready.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":"introducing-eg-ipt-and-ipt-a-novel-electric","repo_url":"https://github.com/nbrochec/ipt_tilde","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"introducing-eg-ipt-and-ipt-a-novel-electric","repo_url":"https://github.com/nbrochec/nime2025","is_official":1,"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}