Construction of a joint peak-interval histogram using higher-order cumulant-based inverse filtering

Conventional metrics used to quantify signals in noise/hearing research rely primarily on time-averaged energy and spectral analyses. Such metrics, while appropriate for Gaussian-distributed waveforms, are of limited value in the more complex sound environments encountered in industrial/military settings that have nonGaussian and nonstationary-distributed waveforms. Research has shown that metrics incorporating the temporal characteristics of a waveform are needed to evaluate hazardous acoustic environments for purposes of hearing conservation. The joint peak-interval histogram is a prospective candidate for use in such an application. This paper shows that the joint peak-interval histogram can be obtained from an estimation of the temporal pattern of a complex noise waveform by using higher-order cumulant-based inverse filtering