Dissonance Spectrum explicitly models perceptual frequency interactions for better music understanding

2026-08-26

AI Summary

该研究通过引入Dissonance Spectrum来显式建模同时频率成分间的关系，以改进音乐理解，并在多项音乐理论测试中表现出色。

Abstract

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.

Problem

• Dissonance Spectrum
• frequency interactions
• music understanding
• time--frequency representation

Innovation

• Dissonance Spectrum
• frequency interactions
• music understanding
• rational pitch-relation kernel
• constant-Q spectrum