On-line genre classification of TV programs using audio content
Automatic genre classification of TV programs can benefit users in various ways such as allowing for rapid selection of multimedia content. In this paper, we introduce an on-line method that can classify genres of TV programs using audio content. We deploy an acoustic topic model (ATM) which was originally designed to capture contextual information embedded within audio segments. With a dataset based on RAI content, we perform both on-line and off-line classification; we segment audio signals with a fixed length and feed into the system for on-line classification tasks, while we use whole audio signals for off-line tasks. The off-line experimental results suggest that the proposed method using audio content yields competitive performance with conventional methods using audio-visual features and outperforms conventional audio-based approaches. The on-line results show promising results in classifying genre of TV programs with short segments and also suggest that ATM performs better than conventional GMM method if the length of audio segments is longer (>1 second).