Harmonic Sonification of High-Dimensional Continuous Data
Author(s)
Dalziel, Tony
Lieck, Robert
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Abstract
High-dimensional continuous data is prevalent across empirical sciences but can be difficult to visualise. Sonification is a compelling alternative with many sonification approaches leveraging custom mappings of specific data features to auditory features. However, this requires 1) prior domain knowledge about the problem of interest and 2) experience with sonification for choosing appropriate auditory features. This makes it difficult to use these approaches as general-purpose methods for sonifying high dimensional continuous data. In this paper, we propose a general-purpose sonification method that exploits the structural similarity between principal component analysis (PCA) and the amplitudes of partials in harmonic overtone spectra. In both cases, the first components are more relevant and tend to have larger values, while later components are less relevant and tend to have smaller values.
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Date
2025-06
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Text
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Proceedings
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Creative Commons Attribution Non-Commercial 4.0 International (CC BY-NC 4.0)