Problem
Research questions and friction points this paper is trying to address.
therapeutic music composition
music interaction data
Autism Spectrum Disorder
reflective composition
sensory responses
ResonaVis: Visualizing Interactive Music Data to Support Reflective Music Composition for Therapeutic Contexts
🤖 AI Summary
This study addresses the prevalent reliance on intuition in therapeutic music composition, which often lacks a systematic understanding of how musical structures relate to listeners’ sensory responses. To bridge this gap, we propose an interactive visualization system that establishes the first design space for visualizing therapeutic music interaction data. By integrating audio features with temporal interaction logs, the system supports reflective composition through coordinated multiple views—temporal, hierarchical co-occurrence, rhythmic activity, and spectral representations—tailored to interactions with autistic children. Employing audio feature extraction, log analysis, and a mixed-methods evaluation, the system achieved a System Usability Scale score of 72.23 and significantly enhanced composers’ confidence in interpreting acoustic and interaction data (p<0.05). It effectively facilitates pattern recognition, hierarchical reasoning, and low-frustration decision-making, thereby advancing data-driven creative practice in music therapy.
📝 Abstract
Designing music for therapeutic contexts requires navigating complex relationships between musical structure and listeners' sensory responses, yet composers often lack structured representations of these interactions, relying instead on intuition. We present ResonaVis, an interactive visualization system that helps composers analyze interaction and audio data from prior sessions with children with Autism Spectrum Disorder (ASD), informing future compositions. ResonaVis integrates audio features and interaction logs to capture how children engage with layered musical compositions, representing this engagement through coordinated visualizations of temporal transitions, layer co-occurrence, rhythmic activity, and spectral characteristics. Rather than prescribing strategies or supporting therapy sessions directly, the system surfaces patterns in past session data to support data-informed reflection during composition. We evaluated ResonaVis through a mixed-methods study with eight music students and a follow-up case study with two experienced composers. Results demonstrate good usability (SUS = 72.23), exceeding benchmarks for early prototypes, and show that participants could identify interaction patterns, reason about layer relationships, and make informed compositional decisions with high perceived performance and low frustration. Confidence in interpreting interaction and acoustic data for ASD-focused composition increased significantly across multiple dimensions (p < 0.05), with qualitative findings suggesting a shift toward more adaptive, data-informed composition. This work contributes a visualization design space for therapeutic music interaction data, an integrated system for compositional reflection, and empirical evidence that visualization tools support analytical reasoning and confidence in data-informed creative practice. (Abstract shortened for arXiv.)
Innovation
Methods, ideas, or system contributions that make the work stand out.
interactive visualization
therapeutic music composition
audio-interactive data integration
autism spectrum disorder (ASD)
reflective design
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