Title:
Exploring sonic parameter mapping for network data structures

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Hansen, Brian
Baltaxe-Admony, Leya Breanna
Kurniawan, Sri
Forbes, Angus G.
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Abstract
In this paper, we explore how sonic features can be used to represent network data structures that define relationships between elements. Representations of networks are pervasive in contemporary life (social networks, route planning, etc), and network analysis is an increasingly important aspect of data science (data mining, biological modeling, deep learning, etc). We present our initial findings on the ability of users to understand, decipher, and recreate sound representations to support primary network tasks, such as counting the number of elements in a network, identifying connections between nodes, determining the relative weight of connections between nodes, and recognizing which category an element belongs to. The results of an initial exploratory study (n=6) indicate that users are able to conceptualize mappings between sounds and visual network features, but that when asked to produce a visual representation of sounds users tend to generate outputs that closely resemble familiar musical notation. A more in-depth pilot study (n=26) more specifically examined which sonic parameters (melody, harmony, timbre, rhythm, dynamics) map most effectively to network features (node count, node classification, connectivity, edge weight). Our results indicate that users can conceptualize relationships between sound features and network features, and can create or use mappings between the aural and visual domains.
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2019-06
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Licensed under Creative Commons Attribution Non-Commercial 4.0 International License.