不协和频谱（Dissonance Spectrum，DS）：显式建模感知频率交互以实现更好的音乐理解
Dissonance Spectrum explicitly models perceptual frequency interactions for better music understanding
- Beijing Institute for General Artificial Intelligence（北京通用人工智能研究院）
- Central Conservatory of Music（中央音乐学院）
- Peking University（北京大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出不协和频谱（DS）这一时频表示，经实验验证其在音乐相关任务中性能优于基线等方法，可作为音乐理解的可解释互补表示。
AI中文摘要：
传统音乐表示描述了能量随时间和频率的变化，但未显式呈现同时频率分量间的关系。我们提出不协和频谱（Dissonance Spectrum，DS），这是一种非负时频表示，它将基于容忍度的有理音高关系核（带有对数谐波距离）应用于恒定Q谱，并将聚合的成对交互归因于各个频率仓。受控音乐理论测试显示，其在音程、和声功能关联及教会调式上具有强序数一致性，在不同和弦转位上则有较弱但显著的一致性。随后，DS由一个轻量并行分支编码，该分支的零初始化残差投影在初始化时保留基线函数。在开放域音乐问答、分类及维度音乐情感识别的6组配对训练种子中，DS在所有报告的端点上均取得了比未改变基线、参数匹配的高斯输入分支及架构匹配的幅度-CQT分支更高的均值。这些结果支持DS作为一种可解释的互补表示，而听众特定感知及更广泛任务覆盖仍是未解决的问题。
英文摘要：
Conventional music representations describe acoustic energy over time and frequency but do not explicitly expose relations among simultaneous frequency components. We introduce the \emph{Dissonance Spectrum} (DS), a nonnegative time--frequency representation that applies a tolerance-based rational pitch-relation kernel with logarithmic harmonic distance to a constant-Q spectrum and attributes aggregate pairwise interactions back to individual frequency bins. Controlled music-theory tests show strong ordinal agreement for intervals, harmonic-function connections, and church modes, and weaker but significant agreement across diverse chord voicings. DS is then encoded by a lightweight parallel branch whose zero-initialized residual projection preserves the baseline function at initialization. Across six paired training seeds in open-ended music question answering and categorical and dimensional music emotion recognition, DS obtains the highest mean on every reported endpoint relative to the unchanged baseline, a parameter-matched Gaussian-input branch, and an architecture-matched magnitude-CQT branch. These results support DS as an interpretable, complementary representation, while listener-specific perception and broader task coverage remain open problems.
