Introducing EG-IPT and ipt~: a novel electric guitar dataset and a new Max/MSP object for real-time classification of instrumental playing techniques

Marco Fiorini, Nicolas Brochec, Joakim Borg, and Riccardo Pasini

Proceedings of the International Conference on New Interfaces for Musical Expression

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 human-computer 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.

Citation

Marco Fiorini, Nicolas Brochec, Joakim Borg, and Riccardo Pasini. 2025. Introducing EG-IPT and ipt~: a novel electric guitar dataset and a new Max/MSP object for real-time classification of instrumental playing techniques. Proceedings of the International Conference on New Interfaces for Musical Expression. DOI: 10.5281/zenodo.15699591 [PDF]

BibTeX Entry

@article{nime2025_14,
 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 human-computer 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.},
 address = {Canberra, Australia},
 articleno = {14},
 author = {Marco Fiorini and Nicolas Brochec and Joakim Borg and Riccardo Pasini},
 booktitle = {Proceedings of the International Conference on New Interfaces for Musical Expression},
 doi = {10.5281/zenodo.15699591},
 editor = {Doga Cavdir and Florent Berthaut},
 issn = {2220-4806},
 month = {June},
 numpages = {9},
 pages = {99--107},
 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},
 track = {Paper},
 url = {http://nime.org/proceedings/2025/nime2025_14.pdf},
 year = {2025}
}