Content-aware visualizations of audio data in diverse contexts




Ness, Steven

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The visualization of the high-dimensional feature landscapes that are encountered when analyzing audio data is a challenging problem and is the focus of much research in the field of Music Information Retrieval. Typical feature sets extracted from sound have anywhere from dozens to hundreds of dimensions and have complex interrelationships between data elements. In this work, we apply various modern techniques for the visualization of audio data to a number of diverse problem domains, including the bioacoustics of Orcinus Orca (killer whale) song, partially annotated chant traditions including Torah recitation and the the analysis of music collections and live DJ sets. We also develop a number of graphical user interfaces to allow users to interact with these visualizations. These interfaces include Flash-enabled web applications, desktop applications, and novel interfaces including the use of the Radiodrum, a three-dimension position sensing musical interface.



music information retrieval, content-aware visualizations