Learning the Helix Topology of Musical Pitch

Abstract

To explain the consonance of octaves, music psychologists represent pitch as a helix where azimuth and axial coordinate correspond to pitch class and pitch height respectively. This article addresses the problem of discovering this helical structure from unlabeled audio data. We measure Pearson correlations in the constant-Q transform (CQT) domain to build a K-nearest neighbor graph between frequency subbands. Then, we run the Isomap manifold learning algorithm to represent this graph in a three-dimensional space in which straight lines approximate graph geodesics. Experiments on isolated musical notes demonstrate that the resulting manifold resembles a helix which makes a full turn at every octave. A circular shape is also found in English speech, but not in urban noise. We discuss the impact of various design choices on the visualization: instrumentarium, loudness mapping function, and number of neighbors K.

Time–frequency scattering accurately models auditory similarities between instrumental playing techniques

“…The former is the time variable while the latter is the base-two logarithm of frequency: u = log 2 λ. Note that u roughly corresponds to the human perception of relative pitch [67].…”

Time-Frequency Scattering Accurately Models Auditory Similarities Between Instrumental Playing Techniques

“…The former is the time variable while the latter is the base-two logarithm of frequency: u = log 2 λ. Note that u roughly corresponds to the human perception of relative pitch [64].…”