Noise reduction in audio employing spectral unpredictability measure and neural net

Abstract

Improvements of the recently presented noise reduction algorithm, based on perceptual coding of audio are revealed. Enhancements of the spectral Unpredictability Measure parameter calculation, which is one of the significant elements in the applied psychoacoustic model are discussed. A learning decision algorithm based on a neural network is employed for determining input signal useful components acting as maskers of the spectral components classified as noise. A new iterative algorithm for calculating the masking pattern is presented. The results of experiments carried out employing the modified algorithm are discussed and conclusions are added. © Springer-Verlag 2004.