Conventional metrics used to quantify signals in noise/hearing research are primarily derived from time- averaged energy and spectral analyses. Such metrics, while appropriate for Gaussian signals, are of limited value in more complex sound environments. Many of the sounds encountered in industrial/military environments have non- Gaussian and nonstationary distributed waveforms. These signals may have the same energy and spectra as those of a continuous Gaussian signal, yet they can produce very different effects on the auditory system. This result has led to efforts to develop additional metrics, incorporating the temporal characteristics of a signal, that could be useful in evaluating hazardous acoustic environments. Previous research suggests that frequency domain kurtosis (FDK) may be useful in such an application. This paper shows that good estimates of FDK can be obtained from an application of the wavelet transform. The wavelet transform, which has features in common with the cochlear micromechanical analysis of a signal, will reflect the temporal variations of the frequency components in a signal. A signal is decomposed by the wavelet transform on a logarithmic scale, and then the fourth-order kurtosis estimates are computed across the different octave bands from the wavelet transform results. Complex signals whose effects on hearing are known, and which are similar to realistic industrial noises, are used as model signals from which the FDK metric is extracted using the wavelet transform. Animal model experiments have shown that FDK is highly correlated with both the frequency specificity of hearing loss and the severity of trauma. Use of the wavelet transform to obtain an FDK metric lends itself to incorporation into digital analysis systems that may be useful in the assessment of complex noises for hearing conservation purposes.