Input-Envelope-Output: Auditable Generative Music Rewards in Sensory-Sensitive Contexts

2026-02-26

AI Summary

This work addresses the challenge of accommodating highly variable auditory tolerance among individuals with sensory sensitivities—such as those with autism—while simultaneously ensuring safety and enabling meaningful participation in interactive audio experiences. To this end, we propose a constraint-first Input–Envelope–Output (I-E-O) framework that inserts a verifiable safety envelope between user input and audio generation, explicitly modeling and enforcing auditory safety boundaries without compromising causal traceability for behavioral auditing. Guided by four verifiable design principles, we implement MusiBubbles, a web-based interactive music system featuring deterministic constraint enforcement and an intervention logging mechanism. The resulting reproducible prototype and accompanying toolkit constitute the first safe, auditable, and generalizable generative music interaction solution tailored for sensory-sensitive contexts.

Technology Category

Natural Language Processing: Safety and RobustnessHumans and AI: Interaction Techniques and DevicesPhilosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

Responsible Web: Machine-in-the-loop, human agency and autonomySecurity and Privacy: Data transparency and provenanceUser Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

Generative feedback in sensory-sensitive contexts poses a core design challenge: large individual differences in sensory tolerance make it difficult to sustain engagement without compromising safety. This tension is exemplified in autism spectrum disorder (ASD), where auditory sensitivities are common yet highly heterogeneous. Existing interactive music systems typically encode safety implicitly within direct input-output (I-O) mappings, which can preserve novelty but make system behavior hard to predict or audit. We instead propose a constraint-first Input-Envelope-Output (I-E-O) framework that makes safety explicit and verifiable while preserving action-output causality. I-E-O introduces a low-risk envelope layer between user input and audio output to specify safe bounds, enforce them deterministically, and log interventions for audit. From this architecture, we derive four verifiable design principles and instantiate them in MusiBubbles, a web-based prototype. Contributions include the I-E-O architecture, MusiBubbles as an exemplar implementation, and a reproducibility package to support adoption in ASD and other sensory-sensitive domains.

Problem

• sensory-sensitive contexts
• auditory sensitivities
• autism spectrum disorder
• generative music
• auditable safety

Innovation

• Input-Envelope-Output
• auditable generative music
• sensory-sensitive design
• safety envelope
• autism spectrum disorder