A Hierarchical Harmonic Mixing Method

Gilberto Bernardes, Matthew E.P. Davies, Carlos Guedes

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We present a hierarchical harmonic mixing method for assisting users in the process of music mashup creation. Our main contributions are metrics for computing the harmonic compatibility between musical audio tracks at small- and large-scale structural levels, which combine and reassess existing perceptual relatedness (i.e., chroma vector similarity and key affinity) and dissonance-based approaches. Underpinning our harmonic compatibility metrics are harmonic indicators from the perceptually-motivated Tonal Interval Space, which we adapt to describe musical audio. An interactive visualization shows hierarchical harmonic compatibility viewpoints across all tracks in a large musical audio collection. An evaluation of our harmonic mixing method shows our adaption of the Tonal Interval Space robustly describes harmonic attributes of musical instrument sounds irrespective of timbral differences and demonstrates that the harmonic compatibility metrics comply with the principles embodied in Western tonal harmony to a greater extent than previous approaches.

Original languageEnglish (US)
Title of host publicationMusic Technology with Swing - 13th International Symposium, CMMR 2017, Revised Selected Papers
EditorsMatthew E.P. Davies, Mitsuko Aramaki, Richard Kronland-Martinet, Sølvi Ystad
PublisherSpringer Verlag
Number of pages20
ISBN (Print)9783030016913
StatePublished - 2018
Event13th international Symposium on Computer Music Multidisciplinary Research, CMMR 2017 - Matosinhos, Portugal
Duration: Sep 25 2017Sep 28 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11265 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other13th international Symposium on Computer Music Multidisciplinary Research, CMMR 2017


  • Audio content analysis
  • Digital DJ interfaces
  • Music information retrieval
  • Music mashup

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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