AudioMetro: directing search for sound designers through content-based cues

Sound designers source sounds in massive collections, heavily tagged by themselves and sound librarians. For each query, once successive keywords attained a limit to filter down the results, hundreds of sounds are left to be reviewed. AudioMetro combines a new content-based information visualization technique with instant audio feedback to facilitate this part of their workflow. We show through user evaluations by known-item search in collections of textural sounds that a default grid layout ordered by filename unexpectedly outperforms content-based similarity layouts resulting from a recent dimension reduction technique (Student-t Stochastic Neighbor Embedding), even when complemented with content-based glyphs that emphasize local neighborhoods and cue perceptual features. We propose a solution borrowed from image browsing: a proximity grid, whose density we optimize for nearest neighborhood preservation among the closest cells. Not only does it remove overlap but we show through a subsequent user evaluation that it also helps to direct the search. We based our experiments on an open dataset (the OLPC sound library) for replicability.