A Discriminative and Compact Audio Representation for Event Detection

Abstract

This paper presents a novel two-phase method for audio representation: Discriminative and Compact Audio Representation (DCAR). In the first phase, each audio track is modeled using a Gaussian mixture model (GMM) that includes several components to capture the variability within that track. The second phase takes into account both global structure and local structure. In this phase, the components are rendered more discriminative and compact by formulating an optimization problem on Grassmannian manifolds, which we found represents the structure of audio effectively. Experimental results on the YLI-MED dataset show that the proposed DCAR representation consistently outperforms state-of-the-art audio representations: i-vector, mv-vector, and GMM.