Efficient Advertisement Discovery for Audio Podcast Content Using Candidate Segmentation

Nowadays, audio podcasting has been widely used by many online sites such as newspapers, web portals, journals, and so forth, to deliver audio content to users through download or subscription. Within 1 to 30 minutes long of one podcast story, it is often that multiple audio advertisements (ads) are inserted into and repeated, with each of a length of 5 to 30 seconds, at different locations. Automatic detection of these attached ads is a challenging task due to the complexity of the search algorithms. Based on the knowledge of typical structures of podcast contents, this paper proposes a novel efficient advertisement discovery approach for large audio podcasting collections. The proposed approach offers a significant improvement on search speed with sufficient accuracy. The key to the acceleration comes from the advantages of candidate segmentation and sampling technique introduced to reduce both search areas and number of matching frames. The approach has been tested over a variety of podcast contents collected from MIT Technology Review, Scientific American, and Singapore Podcast websites. Experimental results show that the proposed algorithm archives detection rate of 97.5% with a significant computation saving as compared to existing state-of-the-art methods.