Abstract

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 …

Date

May 26, 2013

Authors

Samuel Kim, Panayiotis Georgiou, Shrikanth Narayanan

Conference

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

Pages

798-802

Publisher

IEEE