AI 中文总结
研究针对编曲发展中样本检索问题，提出Reflector交互式音频工作站，通过固定音级类预言机及相关技术跟踪和声组合、调整检索，能揭示和声关系，管道本地运行且免费开源，其嵌入保留判断并覆盖整个库。
AI 中文摘要
样本检索工具可帮助作曲家找到和声兼容的素材，但随着编曲的发展以及和声语境随每个音乐决策而变化，从固定参考样本进行查询的信息量会减少。我们提出了Reflector，这是一个交互式音频工作站，它能跟踪和声组合在作曲家时间线上的积累情况，并随着编曲的发展调整检索。该系统围绕一个固定的音级类预言机构建，一个手工设计的权重表，用于对不同来源之间的音级内容组合进行评分。一个完全在合成音频上训练的编码器在128维嵌入空间中学习近似预言机，其中点积以交互速度代表兼容性分数。当作曲家在多轨时间线上编排素材时，扫描线分析会发现同时发声的区域，计算预言机加权质心，并根据会话不断演变的复合和声特征进行检索。投影到可导航3D空间中的会话质心揭示了作曲家作品中的结构和声关系。本文是一个系统阐述：给出每个架构决策的设计原理，通过对一个工作样本库的内在测量来描述Reflector的行为，并介绍实现方法。特征描述得出一个核心发现：学习到的嵌入保留了内核的成对判断，同时覆盖了整个库，而直接将内核用作检索规则时无法做到这一点，因为嵌入的归一化几何无法表达直接评分所青睐的退化解。整个管道在本地运行，无需版权训练数据。Reflector是免费的且训练管道是开源的。
英文摘要
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.
Comments15 pages, 7 figures, 1 table, code, application, and other resources at https://github.com/austinrockman/reflector
