Subbands audio signal recovering using neural nonlinear prediction

Audio signal recovery is a common problem in the digital audio restoration field, because of corrupted samples that must be replaced. In this paper a subband architecture is presented for audio signal recovery, using neural nonlinear prediction based on adaptive spline neural networks. The experimental results show the mean square reconstruction error, and maximum error obtained with increasing gap length, from 200 to 5000 samples. The method gives good results allowing the reconstruction of over 100 ms of signal with low audible effects in overall quality.