We consider the problem of denoising and classification of SONAR signals observed under compositional
noise, i.e., they have been warped randomly along the x-axis. The traditional techniques
do not account for such noise and, consequently, cannot provide a robust classification of signals. We
apply a recent framework that: (1) uses a distance-based objective function for data alignment and
noise reduction, and (2) leads to warping-invariant distances between signals for robust clustering and
classification. We use this framework to introduce two distances that can be used for signal classification:
(a) a y-distance, which is the distance between the aligned signals, and (b) an x-distance that measures
the amount of warping needed to align the signals. We focus on the task of clustering and classifying
objects, using acoustic spectrum (acoustic-color), which is complicated by the uncertainties in aspect
angles at data collections. Small changes in the aspect angles corrupt signals in a way that amounts to
compositional noise. We demonstrate the use of the developed metrics in classification of acoustic color
data and highlight improvements in signal classification over current methods