Characterization of Multiple Transient Acoustical Sources From Time-Transform Representations

Abstract

This paper introduces a new framework for detecting, classifying, and estimating the signatures of multiple transient acoustical sources from a time-transform representation (TTR) of an audio waveform. A TTR is a vector observation sequence containing the coefficients of consecutive windows of data with respect to known sampled basis waveforms. A set of likelihood ratio tests is hierarchically applied to each time slice of a TTR to detect and classify signals in the presence of interference. Since the signatures of each acoustical event typically span several adjacent dependent observations, a Kalman filter is used to generate the parameters necessary for computing the likelihood values. The experimental results of applying the proposed method to a problem of detecting and classifying man-made and natural transient acoustical events in national park soundscape recordings attest to its effectiveness at performing the aforementioned tasks.