Signal representation is crucial for designinga speechrecognizer. The feature extractor selects the information to be used by the classifier to perform the recognition. In noisy environments, the data vectors representing the speech signal are changed and the recognizer performance is degraded by two main facts: (1) the mismatch between the training and the recognition conditions and (2) the de...

We propose a signal representation for transmit-receive uniform linear arrays operating over arbitrary time-varying spatial multipath channels encountered in mobile communications. The signal representation decomposes the multipath channel into a nite number of virtual channels that correspond to certain delays, Doppler shifts, angles that are xed a priori and are independent of the physical ch...

The aim of this paper is to present a new method for the estimation of the instantaneous frequency of a frequency modulated signal, corrupted by additive noise. This method represents an example of fusion of two theories: the timefrequency representations and the mathematical morphology. Any time-frequency representation of a useful signal is concentrated around its instantaneous frequency law ...

We extend the iterated transformation theory (ITT ) fractal image coding algorithm proposed by A. Jacquin [1] to generate a pyramid image representation. An ITT coded image is modeled as the solution of a second kind functional equation. This representation is iterated to form an ITT chain of functional equations which can serve as the framework for a multiscale signal decomposition. This forma...

Forensic audio recordings may contain undesired noise that can impair source identification, speech recognition, and other audio processing requirements. In this paper several custom analysis/synthesis algorithms are presented based on a time-varying spectral representation of the noisy signal. The enhancement process adapts to the instantaneous signal behavior and alters the noisy signal so th...

A time–frequency analysis method to study electromagnetic scattering is presented and demonstrated using canonical objects. The time–frequency analysis method utilizes the Bargmann transform to formulate the signal representation in phase space. The use of the Bargmann transform leads to an attractive parametric signal representation in terms of complex polynomials, and elliptical filters can b...

Non-stationary signal classification is a complex problem. This problem becomes even more difficult if we add the following hypothesis: each signal includes a discriminant waveform, the time location of which is random and unknown. This is a problem that may arise in Brain Computer Interfaces (BCI) or in electroencephalogram recordings of patients prone to epilepsy. The aim of this article is t...

Speech is the most interesting and one of the most complex sounds dealt with by the auditory system. The neural representation of speech needs to capture those features of the signal on which the brain depends in language communication. Here we describe the representation of speech in the auditory nerve and in a few sites in the central nervous system from the perspective of the neural coding o...

Ordinary images, as well as most natural and manmade signals, are compressible and can, therefore, be well represented in a domain in which the signal is sparse. Sparse signal representations have found use in a large number of applications including image compression. Inspired by recent theoretical advances in sparse representation, we propose an image compression using wavelet, sparse represe...

We describe the IC implementation and testing of a lowpower adaptive FIR filter. A new technique is used in its implementation, one that can be employed in other digital signal processing applications where input signals have large dynamic ranges. We propose the use of a reduced 2’s complement signal representation to conditionally disable the internal signal transitions in the most-significant...