Subbands audio signal recovering using neural nonlinear prediction

Abstract

Audio signal recovery is a common problem in digital audio restoration field, because of corrupted samples that must be replaced. In this paper a subbands 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 100ms signal with low audible effects in overall quality.

Optimizing dictionary learning parameters for solving Audio Inpainting problem

“…For example distorted or missing signal recover was repaired by interpolation techniques [1], samples repetition, wavelet transform [2] or neural networks [3]. IP telephony is a common example of packet loss problem where data is lost during the transmition and they have to be recovered, e.g.…”