Auditory modeling via frequency warped wavelet transform

In this paper we present an approach to auditory modeling based on frequency warped wavelet transforms. This set of wavelets overcomes the limitations of ordinary wavelets given by octave band resolution or by rational sampling rate filterbanks. The frequency bandwidths of the basis sequences may be arbitrarily designed by selecting a single parameter for each scale level. This parameter controls the pole position of a chain of identical first order all-pass filters, which is the computational structure of the Laguerre transform. The Laguerre parameter determines the amount of warping necessary at each scale in order to match the prescribed cutoff frequencies. Bark, mei or other filterbanks based on perceptual criteria can be exactly implemented by means of our transform, taking full advantage of the othogonality and completeness of the transform.