Parametric audio quality estimation models for broadcasting systems and web-casting applications based on the Genetic Programming

The COVID-19 pandemic has been one of the biggest disruptions to education that the world has ever experienced, affecting the most of the world student population. Many countries turned to online based distance education to ensure that learning never stops. As a consequence, throughout the globe there has been an increasing trend among the students to use different broadcasting systems and web-casting applications for the purpose of online learning. However, the video or audio quality that these various applications offer will be the key factor for their acceptance, i.e. whether or not the students will be willing to use those systems for online learning. Therefore, a machine learning technique, i.e. Genetic Programming, is used in this work for the purpose of assessing audio quality using an objective approach. A design and performance evaluation of the parametric models estimating the audio quality perceived by the end user of broadcasting systems and web-casting applications are presented in this paper. To estimate the quality of audio broadcasting systems and web-casting applications, a set of parameters influencing the quality is used as an input for the developed parametric quality estimation models. The results obtained by the developed parametric audio quality estimation models have validated Genetic Programming as a powerful technique, providing a good accuracy and generalization capabilities. This makes it a possible candidate for the estimation of audio quality perceived by the end user in the context of the broadcasting systems and web-casting applications.