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This paper presents a novel two-phase method for audio representation,Discriminative and Compact Audio Representation (DCAR), and evaluates itsperformance at detecting events in consumer-produced videos. In the first phaseof DCAR, each audio track is modeled using a Gaussian mixture model (GMM) thatincludes several components to capture the variability within that track. Thesecond phase takes into account both global structure and local structure. Inthis phase, the components are rendered more discriminative and compact byformulating an optimization problem on Grassmannian manifolds, which we foundrepresents the structure of audio effectively.  Our experiments used the YLI-MED dataset (an open TRECVID-style video corpusbased on YFCC100M), which includes ten events. The results show that theproposed DCAR representation consistently outperforms state-of-the-art audiorepresentations. DCAR's advantage over i-vector, mv-vector, and GMMrepresentations is significant for both easier and harder discrimination tasks.We discuss how these performance differences across easy and hard cases followfrom how each type of model leverages (or doesn't leverage) the intrinsicstructure of the data. Furthermore, DCAR shows a particularly notable accuracyadvantage on events where humans have more difficulty classifying the videos,i.e., events with lower mean annotator confidence.

DCAR: A Discriminative and Compact Audio Representation to Improve Event Detection