Most machine speech analysis and processing is based on a warped spectral representation. The intent of the paper is to present a method by which proper warped representations can be computed efficiently. In the case of log-warping functions, the methods of the paper produce a wavelet-like transform as a linear convolution of a single log-warped wavelet basis element and a log-warped representation of the signal. The resulting doubly warped transform is referred to in the paper as a Mellin-wavelet transform. The majority of the paper is devoted to deriving design parameters for implementation of the transform, with speech as the primary application.