An Overview of Fractal Processing of Noise-Like Auditory Signals

Abstract

This paper presents an overview of fractal signal processing for characterizing noise-like auditory signals. Fractal processing is particularly effective for signals exhibiting a kind of self-similarity property. This paper identifies four aspects of signals that may have such a property: (i) signal sets, (ii) signal spectra, (iii) signal measures, and (iv) signal sources. We describe a self-similar characterization of each aspect, and discuss their applicability in processing noise-like signals, especially for intended respiratory pre-screening applications.