ASEKS: A P2P Audio Search Engine Based on Keyword Spotting

Currently, most search engines are text-based and their structures are centralized. These kinds of engine are sufficient for searching text information in Internet. However, while searching audio resource, an efficient content-based audio search engine is required. In this paper, we demonstrate an audio search engine ASEKS based on keyword spotting technology in the peer-to-peer (P2P) network. The indexing sub-model spots information in local audio files and generates indices for later query; and the P2P networks distributes the query and gathers the results. ASEKS supports scalability and avoids the bottleneck of network load that usually exists in centralized architecture. The average accuracy of the keyword spotting sub-model is 88.4% in detection rate on the 5.267 false alarm per keyword per hour