Bandwidth extension of narrowband speech in log spectra domain using neural network

Authors : Sara POURMOHAMMADI, Mansour VALI, Mohsen GHADYANI

Pages : 433-446

Doi:10.3906/elk-1212-109

View : 11 | Download : 3

Publication Date : 0000-00-00

Article Type : Research Paper

Abstract :In recent years, there have been significant advances in communication technology, but speech signals still suffer from low perceived quality caused by bandwidth limitations of telephone networks. The bandwidth extension insert ignore into journalissuearticles values(BWE); approach adds high-frequency components of the speech signal to band-limited telephone speech and increases speech perception significantly. In this work, we develop a new method for representation of vocal tract filter coefficients using log of filter bank energy insert ignore into journalissuearticles values(LFBE); parameters as an alternative for mel-frequency cepstral coefficients insert ignore into journalissuearticles values(MFCCs);. This approach is based on a strong correlation between the spectral components of low- and high-band spectrums. Furthermore, the performances of Gaussian mixture model and multilayer perceptron neural network methods for estimation of the high-frequency envelope are evaluated. Objective evaluations of the obtained results indicate that the LFBE feature vectors have better performance than the MFCCs. In addition, findings of the objective evaluations showed that using a neural network, which is not common in BWE, achieves a better performance as compared to the Gaussian mixture model.
Keywords : Bandwidth extension, log spectra domain, narrowband speech, neural network, wideband speech