Learning the helix topology of musical pitch
Learning the helix topology of musical pitch
Learning the helix topology of musical pitch
In this paper, we present a method to embed frequency bins from an audio dataset in a Euclidean embedding space, testing on music, speech and urban sounds.
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We find that highly harmonic instruments such as harp yield a perfectly 'helical' embedding, while urban soundscapes yield rectilinear topologies.
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I presented this paper at the virtually-held IEEE ICASSP 2020 Conference.
Helicality: An Isomap-based Measure of Octave Equivalence
in Audio Data
To better compare embedding results from different datasets, we introduce the "helicality" algorithm which aims to quantify the notion of octave equivalence.
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We define helicality as a point-cloud's goodness of fit to a Shepard-Risset helix, testing on monophonic instrument recordings, speech and drums.
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I presented this Late-breaking demo at the ISMIR Conference 2020.
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