TY - GEN
T1 - A Hierarchical Harmonic Mixing Method
AU - Bernardes, Gilberto
AU - Davies, Matthew E.P.
AU - Guedes, Carlos
N1 - Funding Information:
Acknowledgments. This work is supported by national funds through the FCT - Foundation for Science and Technology, I.P., under the project IF/01566/2015.
Publisher Copyright:
© 2018, Springer Nature Switzerland AG.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - Audio content analysis
KW - Digital DJ interfaces
KW - Music information retrieval
KW - Music mashup
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U2 - 10.1007/978-3-030-01692-0_11
DO - 10.1007/978-3-030-01692-0_11
M3 - Conference contribution
AN - SCOPUS:85057394557
SN - 9783030016913
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 151
EP - 170
BT - Music Technology with Swing - 13th International Symposium, CMMR 2017, Revised Selected Papers
A2 - Davies, Matthew E.P.
A2 - Aramaki, Mitsuko
A2 - Kronland-Martinet, Richard
A2 - Ystad, Sølvi
PB - Springer Verlag
T2 - 13th international Symposium on Computer Music Multidisciplinary Research, CMMR 2017
Y2 - 25 September 2017 through 28 September 2017
ER -