A Vector-Based Approach to Broadcast Audio Database Indexing and Retrieval

This paper proposes a novel framework to index and retrieve audio content from broadcast database that contains both speech and music. In this framework, we model the acoustic events using hidden Markov models, which are then used to decode the audio content. The decoding results in the form of acoustic token sequence and acoustic lattice are used to generate features for indexing and retrieval with the vector space model. Experiments were carried out on the TRECVID database and the results showed that the proposed framework is effective in audio information retrieval. The results also showed that the features generated from the acoustic lattice provide more accurate information than token sequence.