Seeing Beyond Sound: Visualization and Abstraction in Audio Data Representation
Ashlae Blum'e
TL;DR
This paper addresses the misalignment between human auditory perception and traditional 2D audio visualizations, along with the opacity of legacy software. It proposes design principles—transparency, flexibility, and robustness—grounded in cognitive load theory and visual design, and demonstrates these ideas with Jellyfish Dynamite, a Python-based, interactive tool offering multiple spectral transforms and MVC architecture. By enabling simultaneous, multisensory representations and user-driven exploration, the work aims to improve pattern recognition, foster inclusivity (including citizen science), and support collaborative workflows in audio information research. The practical impact is a more adaptable, transparent, and engaging framework for analyzing complex audio data across professional, educational, and public contexts.
Abstract
In audio signal processing, the interpretation of complex information using visual representation enhances pattern recognition through its alignment with human perceptual systems. Software tools that carry hidden assumptions inherited from their historical contexts risk misalignment with modern workflows as design origins become obscured. We argue that creating tools that align with emergent needs improves analytical and creative outputs due to an increased affinity for using them. This paper explores the potentials associated with adding dimensionality and interactivity into visualization tools to facilitate complex workflows in audio information research using the Jellyfish Dynamite software.