Two-class verifier framework for audio indexing

We address the problem of audio indexing for a class of special-case scenarios, where it is required to index an audio stream into 2 classes, namely, a target class and a background class, as arising, say in, audio surveillance and machine diagnostics. With the emphasis on dealing with limited training exemplars defining the target class in these scenarios, we propose a 2-class `audio verification´ framework, where the target and background classes are modeled by GMMs and the indexing is done via a sliding window based detection. We characterize the performance of the system in terms of ROCs, EERs and visual detection plots for a set of 2 target classes and 4 background classes from a surveillance audio database and show the viability of such a system in practical applications. We highlight the robustness of the system to high levels of background-class using visual detection plots of continuous audio streams at SNRs ranging from 30 dB down to -20 dB.