top of page
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.
We find that highly harmonic instruments such as harp yield a perfectly 'helical' embedding, while urban soundscapes yield rectilinear topologies.
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.
We define helicality as a point-cloud's goodness of fit to a Shepard-Risset helix, testing on monophonic instrument recordings, speech and drums.
I presented this Late-breaking demo at the ISMIR Conference 2020.
bottom of page