Reflector: Arrangement-Aware Harmonic Retrieval for Sample-Based Composition

• URL: http://arxiv.org/abs/2607.22413v1
• Date: Fri, 24 Jul 2026 15:30:30 GMT
• Title: Reflector: Arrangement-Aware Harmonic Retrieval for Sample-Based Composition
• Abstract summary: We present Reflector, an interactive audio workstation that tracks harmonic combinations as they accumulate on the composer's timeline.<n>An encoder trained entirely on synthetic audio learns to approximate the oracle in a 128-dimensional embedding space.<n>Session centroids projected into a navigable 3-D space reveal structural harmonic relations across the composer's body of work.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Sample retrieval tools can help composers find harmonically compatible material, but querying from a fixed reference sample becomes less informative as arrangements evolve and the harmonic context shifts with each musical decision. We present Reflector, an interactive audio workstation that tracks harmonic combinations as they accumulate on the composer's timeline and adapts retrieval as the arrangement develops. The system is organized around a fixed interval-class oracle: a hand-designed table of weights that scores how pitch-class content combines between sources. An encoder trained entirely on synthetic audio learns to approximate the oracle in a 128-dimensional embedding space, where dot products stand in for compatibility scores at interactive speed. As the composer arranges material on a multi-track timeline, a sweep-line analysis discovers co-sounding regions, computes oracle-weighted centroids, and retrieves against the composite harmonic identity of the session as it evolves. Session centroids projected into a navigable 3-D space reveal structural harmonic relations across the composer's body of work. This paper is a systems account: we give the design rationale for each architectural decision, characterize Reflector's behavior through intrinsic measurements on a working sample library, and describe the implementation. The characterization yields a central finding: the learned embedding preserves the kernel's pairwise judgments while covering the whole library, something the kernel cannot do when used directly as a retrieval rule, because the embedding's normalized geometry cannot express the degenerate solutions that direct scoring favors. The entire pipeline runs locally with no copyrighted training data. Reflector is free, and the training pipeline is open source.

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