Applications of Neural Networks and Perceptual Masking to Audio Restoration

Applications of learning algorithms to the restoration of recordings are presented. Attention is paid to the usage of artificial neural networks as a decision system determining which components of an input signal are valid and which ones are unwanted. It provides a basis for the parasitic impulse detection and for the interpolation of lost signal intervals. Such an approach enables also an efficient noise reduction employing the extended perceptual coding algorithm. The proposed algorithms are described briefly in the paper, obtained results are discussed and some general conclusions concerning the application of soft computing and perceptual masking to sound restoration are added

Artificial neural network · Audio signal · Auditory masking · Noise reduction · Perception · Speech coding · Speech recognition · Advanced Adaptive Filtering Techniques · Computer Science · Image and Signal Denoising Methods · Mathematics · Neural Networks and Applications · Artificial Intelligence