Recurrent Support Vector Machines for Audio-Based Multimedia Event Detection
Yun Wang, Florian Metze · 2016
Multimedia event detection (MED) is the task of detecting given events (e.g. parade, birthday party) in a large collection of video clips. While the most useful information comes from visual features and speech recognition, a lot can also be inferred from the non-speech audio content, either alone or in conjunction with visual and speech cues. This paper studies MED with non-speech audio information only. MED is usually performed in two stages. The first stage generates a representation for each clip in the form of either a single vector or a sequence of vectors, often by aggregating frame-level features; the second stage performs binary or multi-class classification to decide whether each target event occurs in each clip. Common classifiers used for the second stage include support vector machines (SVMs), feed-forward deep neural networks (DNNs), and recurrent neural networks (RNNs).