A Probabilistic Ranking Model for Audio Stream Retrieval.

In Audio Stream Retrieval (ASR) systems, clients periodically query an audio database with an audio segment taken from the input audio stream to keep track of the flow of the stream in the original content sources or to compare two differently edited streams. We recently developed a series of ASR applications such as broadcast monitoring systems, automatic caption fetching systems, and automatic media edit tracking systems. Based on this experience, we propose a probabilistic ranking model designed for ASR systems. In order to train and test the model, we create a new set of audio streams and make it publicly available. Our experiments with these new streams confirm that the proposed ranking model works effectively with the retrieved results and reduces the errors when used in various ASR applications.