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We pursue the hypothesis that musical differences between cultures are based on linguistic, especially phonological, properties of the culture's spoken language. To study this hypothesis, we present a general constraint-based framework for describing the structural similarities between music and language. Music and language are structured by the fact that some sounds are more important than others, based on cognitive strategies which we present here as universal well-formedness conditions. However, which sounds are considered to be most salient differs across cultures, as evidenced by the world's many linguistic and musical typologies. The first goal of our research approach is to identify these universal well-formedness conditions (e.g. prominence of strong elements based on the syllable/chord structure and domain marking based on intonation/ melody patterns, pauses) for speech and music. The second goal is to assess how cultures differ from each other in terms of the relative salience assigned to these conditions (i.e. how these conditions are "ranked"). The current paper is meant to be an introduction to a new approach with focus on the identification of general well-formedness conditions. We introduce similar conditions for the description of language and music in order to make comparison of the two disciplines more fruitful. The goal of our research approach is to create a theoretical and methodological map to aid more detailed culture-specific comparisons. The ultimate aim is to provide a comprehensive typological overview for which we will start with a selection of culture families following the World Atlas of Language Structures online (Dryer & Haspelmath, 2013) for language and the Global Jukebox (Wood & Arèvalo, 2018) for music.

Purpose This study compares two electromagnetic articulographs manufactured by Northern Digital, Inc.: the NDI Wave System (from 2008) and the NDI Vox-EMA System (from 2020). Method Four experiments were completed: (a) comparison of statically positioned sensors, (b) tracking dynamic movements of sensors manipulated using a motor-driven LEGO apparatus, (c) tracking small and large movements of sensors mounted in a rigid bar manipulated by hand, and (d) tracking movements of sensors rotated on a circular disc. We assessed spatial variability for statically positioned sensors, variability in the transduced Euclidean distances between sensor pairs, and missing data rates. For sensors tracking circular movements, we compared the fit between fitted ideal circles and actual trajectories. Results The average sensor pair tracking error (i.e., the standard deviation of the Euclidean distances) was 1.37 mm for the WAVE and 0.12 mm for the VOX during automated trials at the fastest speed, and 0.35 mm for the WAVE and 0.14 mm for the VOX during the tracking of large manual movements. The average standard deviation of the fitted circle radii charted by manual circular disc movements was 0.72 mm for the WAVE sensors and 0.14 mm for the VOX sensors. There was no significant difference between the WAVE and the VOX in the number of missing frames. Conclusions In general, the VOX system significantly outperformed the WAVE on measures of both static precision and dynamic accuracy (automated and manual). For both systems, positional precision and spatial variability were influenced by the sensors' position relative to the field generator unit (worse when further away).