Dissonance Spectrum explicitly models perceptual frequency interactions for better music understanding

Tianle Wang, Xinyi Tong, Liangke Zhao, Jishang Chen, Sirui Zhang +5 more
8/26/2026
cs.SDcs.AI

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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.

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@article{wang2026dissonance,
  title  = {Dissonance Spectrum explicitly models perceptual frequency interactions for better music understanding},
  author = {Tianle Wang and Xinyi Tong and Liangke Zhao and Jishang Chen and Sirui Zhang and Haoxin Zhang and Xin Jin and Duo Xu and Xiaobing Li and Song-Chun Zhu},
  year   = {2026},
  eprint = {2608.25621},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2608.25621v1}
}

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