A Computational Algorithm Based on Convolutional Neural Networks Aimed at Estimating the MOS Quality Parameter According to the Norm UIT-T P.862
Rodrigo Gutierrez, Brallan Asca, Guillermo Kemper · 2019
This paper proposes an algorithm based on convolutional neural networks for the estimation of the quality level of voice signals transmitted through cellular communication systems. The objective is to take advantage of artificial intelligence methods to estimate the MOS parameter and obtain a similar accuracy to that obtained by methods and procedures established in the international norms and international licensed standards. The proposed algorithm uses the MOS results obtained by the method detailed in the ITU-T P.862 standard. The values were obtained for different signals acquired at different reception points. With this information we proceeded to design and train a convolutional neuronal network of 4 layers, achieving very satisfactory results. For the validation, the mean square error was used to measure the degree of similarity of the MOS values obtained by ITU-T P.862 and by the proposed algorithm. The results show a mean square error of 0.00007 for the proposed algorithm.