2016/07/15 by Liping Jing, Jing, Liping, Bo Liu +11
Computer Science · #FOS: Computer and information sciences #H.5.1 #Multimedia (cs.MM) #Music and Audio Processing #Sound (cs.SD) #Speech and Audio Processing #Video Analysis and Summarization #cs.MM #cs.SD
paper · pdf · doi:10.48550/arxiv.1607.04378
An abbreviated version of this paper will be published in ACM Multimedia 2016
arxiv created 2016/07/15 · openalex publication_date 2016/07/15 · arxiv updated 2016/07/18 · openalex created_date 2016/09/16 · openalex updated_date 2026/07/28
This paper presents a novel two-phase method for audio representation, Discriminative and Compact Audio Representation (DCAR), and evaluates its performance at detecting events in consumer-produced videos. In the first phase of DCAR, 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. Our experiments used the YLI-MED dataset (an open TRECVID-style video corpus based on YFCC100M), which includes ten events. The results show that the proposed DCAR representation consistently outperforms state-of-the-art audio representations. DCAR's advantage over i-vector, mv-vector, and GMM representations is significant for both easier and harder discrimination tasks. We discuss how these performance differences across easy and hard cases follow from how each type of model leverages (or doesn't leverage) the intrinsic structure of the data. Furthermore, DCAR shows a particularly notable accuracy advantage on events where humans have more difficulty classifying the videos, i.e., events with lower mean annotator confidence.