N-GRAM EXTENSION FOR BAG-OF-AUDIO-WORDS

Bag-of-audio-words is one of the most frequently used methods for incorporating an audio component into multimedia event detection and related tasks. A main criticism of the method, however, is that it ignores context. Each "word" is considered in isolation, ignoring its neighbors. We address this issue by representing the document by its audio word N-grams. Unlike words from natural language, audio words are generated by clustering algorithms where the number of clusters is specified by the researcher. We therefore also explore how the performance of the N-gram representation varies with codebook size. With this enhanced representation, we find the average probability of miss noticeably decreases when evaluated on TRECVID 2011 and 2012 datasets, indicating clear improvements on the multimedia event detection task.