Probabilistic Detection Methods for Acoustic Surveillance Using Audio Histograms

Abstract

Acoustic surveillance is gaining importance given the pervasive nature of multimedia sensors being deployed in all environments. In this paper, novel probabilistic detection methods using audio histograms are proposed for acoustic event detection in a multimedia surveillance environment. The proposed detection methods use audio histograms to classify events in a well-defined acoustic space. The proposed methods belong to the category of novelty detection methods, since audio data corresponding to the event is not used in the training process. These methods hence alleviate the problem of collecting large amount of audio data for training statistical models. These methods are also computationally efficient since a conventional audio feature set like the Mel frequency cepstral coefficients in tandem with audio histograms are used to perform acoustic event detection. Experiments on acoustic event detection are conducted on the SUSAS database available from Linguistic data consortium. The performance is measured in terms of false detection rate and true detection rate. Receiver operating characteristics curves are obtained for the proposed probabilistic detection methods to evaluate their performance. The proposed probabilistic detection methods perform significantly better than the acoustic event detection methods available in literature. A cell phone-based alert system for an assisted living environment is also discussed as a future scope of the proposed method. The performance evaluation is presented as number of successful cell phone transactions. The results are motivating enough for the system to be used in practice.