Abstract
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.
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- 1.
The Bark spectrum balances the resolution across the human hearing range in comparison to the typical power spectrum representation, namely increasing the resolution in the low frequency region. It is computed by warping a power spectrum to the 24 critical bands of the human auditory system [28].
- 2.
We used the version 0.9 of IRCAM’s SOL database, retrieved at http://forumnet.ircam.fr/product/orchids-en/ in July, 2017 as the supporting database of the Orchids software.
- 3.
Please refer to https://sites.google.com/site/tonalintervalspace/mixmash to listen to electronic and synthetic instrument sample examples from the NSynth database.
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This work is supported by national funds through the FCT - Foundation for Science and Technology, I.P., under the project IF/01566/2015.
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Bernardes, G., Davies, M.E.P., Guedes, C. (2018). A Hierarchical Harmonic Mixing Method. In: Aramaki, M., Davies , M., Kronland-Martinet, R., Ystad, S. (eds) Music Technology with Swing. CMMR 2017. Lecture Notes in Computer Science(), vol 11265. Springer, Cham. https://doi.org/10.1007/978-3-030-01692-0_11
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