Mapping neural networks for bandwidth extension of narrowband speech

This paper exploits the nonlinear mapping property of feed-forward neural networks for estimation of high frequency components (4-8kHz) of the speech signals from the band-limited (0-4kHz) signals. Cepstral coefficients are used to represent the feature vectors of each frame of data. This paper also proposes an approach that uses the autocorrelation method to derive the Linear Prediction (LP) coefficients from the estimated cepstral coefficients that are obtained from the mapping network. This method guarantees the stability of the LP synthesis filter. Informal listenings indicate the effectiveness of the proposed method for estimation of wideband frequency components of speech. The enhanced speech sounds similar to the original wideband speech. Also, it does not contain any distortion that may arise due to spectral discontinuities between adjacent frames.