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Paper Session 5b: AI, Machine Learning & Pedagogy

May 13 @ 9:00 am - 10:30 am
Session Chair: Rodrigo Cadiz

Paper Abstracts

Jeff Kaiser and Gregory Taylor: “Building Loopers: A Pedagogical Framework for Teaching Creative Software Design Through Iterative Tool Construction in Max, gen~, and RNBO”

This paper introduces the ideas behind our open-access project “Building Live Loopers in Max.”1 The project presents a hybrid pedagogical and technical framework in which students learn signal processing concepts by constructing live-looping tools in Max, gen~, and RNBO. By engaging with buffer operations, timing structures, playback manipulation, and parameter mapping, students develop technical fluency and musical understanding simultaneously. We introduce a sequence of modular, step-by-step looper designs, a color-coded instructional method for visualizing patcher development, and a cross-environment workflow that reinforces transferable pro-
gramming habits. Our coursework is designed to be sufficiently open-ended that students, while grounded in familiar musical contexts, are encouraged to exercise curiosity and explore creative directions beyond the methods presented. Drawing on Dehaene’s work on curiosity and Eagle-
man’s writing on relevance, the design aims to engage intrinsic motivation and support students in forming novel connections and actively experimenting with musical ideas. This approach positions looper construction as a bridge between creative music-making and computational thinking, supporting both performance and pedagogical outcomes.

Nicolas Brochec and Jean-Louis Giavitto: “Automatic Following of Flute Playing Techniques for Real-Time Mixed Music: A Case Study with Antescofo and ipt~”
This paper investigates how real-time recognition of instrumental playing techniques can extend automatic score following beyond the limits of pitch-based alignment. While systems such as Antescofo provide robust and largely plug-and-play score following, their listening model is primarily designed for stable, pitched events aligned with a fixed symbolic score. This makes them difficult to adapt to extended techniques, unpitched sounds, and musical forms involving partial improvisation or open notation. To address these limitations, we explore a hybrid approach that combines multiple listening machines with complementary capabilities and allows dynamic switching between them during performance according to the musical context. Specifically, we integrate Antescofo with ipt˜, a real-time playing technique recognition system based on lightweight machine learning models. We focus on the integration of real-time instrumental playing technique recognition as a means to enrich the listening process and support technique-aware navigation of the score. We evaluate this approach on the case of extended flute techniques, assessing both the feasibility of technique aware following and the trade-off between system generality and performance. Results suggest that learning-based listening modules provide a practical compromise: they improve
robustness for specific techniques while preserving much of the plug-and-play character supporting multiple works and performers. The results highlight a promising balance between generality, specificity, and performative robustness.
Colton Arnold, Zhaohan Cheng and Ajay Kapur: “AI Framework for Dynamic Robotic Instrument Calibration”
This paper presents a data-driven calibration framework for robotic musical instruments based on a hybrid ensemble model that combines K-nearest neighbors (KNN) and a multi-layer perceptron (MLP). KNN anchors predictions to recorded acoustic measurements, while the MLP enables nonlinear generalization and smooth interpolation across the instrument’s playable range. A distance-dependent blending strategy integrates the two models, improving consistency across sparse and dense data. The proposed approach produces stable and repeatable calibration estimates for both pitched and non-pitched instruments, outperforming standalone models across a range of sampling conditions. This work establishes a scalable foundation for automated calibration in robotic musical systems.

 

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