A full-text retrieval approach to content-based audio identification

We give an overview on a novel framework for content–based multimedia retrieval. In this paper we present an implementation for audio identification. This framework consists of an index–based search combining algebraic methods with classical full–text retrieval. In the main part of the paper we propose several feature extractors which may be used for indexing the PCM audio data. We give an overview on our test results containing performance data (e.g., query response times), memory requirements (e.g., index size), and robustness issues. The size of our index turns out to be only a 1/1000th to about 1/15000th of the original PCM material depending on the required granularity for identifying a piece of audio.