A signal processing system was designed to simulate hearing impairment for normal hearing listeners. Typical consequences of sensorineural hearing loss are an elevated threshold in quiet, abnormal growth of loudness ('recruitment') and reduced spectral and temporal resolution. However, the just noticable difference (jnd) in intensity is unaffected. Based on an aurally adequate time-frequency re...

Many multi-dimensional signal processing problems require the computation of signal gradients or directional derivatives. Traditional derivative estimates based on adjacent or central diierences are often inappropriate for multi-dimensional problems. As replacements for these traditional operators, we design a set of matched pairs of derivative lters and lowpass pre-lters. We demonstrate the su...

Transfer function models for the description of physical systems have recently been introduced to the field of multidimensional digital signal processing. They provide an alternative to the conventional representation by partial differential equations (PDE) and are suitable for computer implementation. This paper extends the transfer function approach to vector PDEs. They arise from the physica...

The application of methods from multidimensional systems theory and digital signal processing opens new ways for the eecient simulation of time and space dependent problems like wave propagation or heat and mass transfer. The basic idea is the application of suitably chosen functional transformations for the time and space coordinates. Initial and boundary conditions are considered in a systema...

We propose a general approach to regression on digitized multidimensional signals that can pose severe challenges to standard statistical methods. The main contribution of this work is to build a two-dimensional coefficient surface that allows for interaction across the indexing plane of the regressor array. We aim to use the estimated coefficient surface for reliable (scalar) prediction. We as...

Many multi-dimensional signal processing problems require the computation of signal gradients or directional derivatives. Traditional derivative estimates based on adjacent or central differences are often inappropriate for multi-dimensional problems. As replacements for these traditional operators, we design a set of matched pairs of derivative filters and lowpass prefilters. We demonstrate th...

Classical multidimensional scaling is a simple, linear technique for finding coordinates of points given their interpoint distances. In this paper we describe the algorithm and show how it can be used to solve the geometric microphone array calibration problem. The method requires no complicated hardware or calibration targets, just a tape measure (or similar measuring device). We also extend t...

the record of human brain neural activities, namely electroencephalogram (eeg), is generally known as a non-stationary and nonlinear signal. in many applications, it is useful to divide the eegs into segments within which the signals can be considered stationary. combination of empirical mode decomposition (emd) and hilbert transform, called hilbert-huang transform (hht), is a new and powerful ...

This paper deals with unsupervised Bayesian classification of multidimensional data. We propose an extension of a previous method of generalized mixture estimation to the correlated sensors case. The method proposed is valid in the independent data case, as well as in the hidden Markov chain or field model case, with known applications in signal processing, particularly speech or image processi...