Brandon Mechtley

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We propose a method for characterizing sound activity in fixed spaces through segmentation, indexing, and retrieval of continuous audio recordings. Regarding <i>segmentation</i>, we present a dynamic Bayesian network (DBN) that jointly infers onsets and end times of the most prominent sound events in the space, along with an extension of the algorithm for(More)
We describe the realization of two interactive, mixed-reality installations arising from a partnership of K-12, university, and museum participants. Our goal was to apply emerging technologies to produce an innovative, hands-on arts learning experience within a conventional art museum. Suspended Animation, a Reflection on Calder is a mixed-reality(More)
Recent work in audio information retrieval has demonstrated the effectiveness of combining semantic information, such as descriptive, tags with acoustic content. However, these methods largely ignore the possibility of tag queries that do not yet exist in the database and the possibility of similar terms. In this work, we propose a network structure(More)
Organizing a database of user-contributed environmental sound recordings allows sound files to be linked not only by the semantic tags and labels applied to them, but also to other sounds with similar acoustic characteristics. Of paramount importance in navigating these databases are the problems of retrieving similar sounds using textor sound-based(More)
Many techniques for text-based retrieval and automatic annotation of music and sound effects rely on learning with explicit generalization, training individual classifiers for each tag. Non-parametric approaches, where queries are individually compared to training instances, can provide added flexibility, both in terms of robustness to shifts in database(More)
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