EASIER Sampling for Audio Event Identification

An audio event refers to some specific audio sound which plays important role for video content analysis. In our previous work [3], we have established audio event identification as an audio classification task. Due to the large size of audio database, representative samples are necessary for training the classifier. However, the commonly used random selection of training samples is often not adequate in selecting representative samples. In this paper we present EASIER sampling algorithm to select those data which more efficiently represent audio data characters for audio event identifier training. EASIER deterministically produces a subsample whose “distance” from the complete database is minimal. Experiments in the context of audio event identification show that EASIER outperforms simple random sampling significantly.