- A. Juárez, B. Raj, Rita Singh. 2013. Semi-supervised context-aware discovery of unknown audio concepts. Abstract: Both defining new audio categories and annotating data that belong to these is a problem yet hardly tackled, much less resolved. These problems need to be solved, however, if we are to escalate the labeling of audio data from subjective manual annotation onto automatic audio discovery upon the vast amounts of audio data available today. The lack of work on this matter is understandable: audio data overlaps different semantic categories through a single channel, it is most often noisy, and any application that works on large datasets needs to deal with these problems. Additionally, relating semantic concepts to audio data is a problem in itself: how can a system associate newlyencountered acoustic data to concepts of which there is no data available? In this paper we describe how we used a labeled dataset to train reliable concept detectors for several semantic categories, how we augmented unlabeled data with contextual features through co-occurrence to and duration of known concepts, and results that indicate the feasibility of this task. We believe the design presented can serve as a general discovery framework for audio-like sequential data in general.
