Unsupervised detection of acoustic events using information bottleneck principle

Abstract

An unsupervised approach based on Information Bottleneck (IB) principle is proposed for detecting acoustic events from audio streams. In this paper, the IB principle is first concisely presented, and then the practical issues related to the application of IB principle to acoustic event detection are described in detail, including definitions of various variables, criterion for determining the number of acoustic events, tradeoff between amount of information preserved and compression of the initial representation, and detection steps. Further, we compare the proposed approach with both unsupervised and supervised approaches on four different types of audio files. Experimental results show that the proposed approach obtains lower detection errors and higher running speed compared to two state-of-the-art unsupervised approaches, and is little inferior to the state-of-the-art supervised approach in terms of both detection errors and runtime. The advantage of the proposed unsupervised approach over the supervised approach is that it does not need to pre-train classifiers and pre-know any prior information about audio streams.