A Benchmark of Non-intrusive Parametric Audio Quality Estimation Models for Broadcasting Systems and Web-casting Applications
Due to the rising usage of various broadcasting systems and web-casting applications, a measurement of audio quality has become an essential task. This paper presents a benchmark of the parametric models for non-intrusive estimation of the audio quality perceived by the end user. The proposed soluti...
Saved in:
Published in | Advances in electrical and electronic engineering Vol. 19; no. 4; pp. 304 - 312 |
---|---|
Main Authors | , |
Format | Journal Article |
Language | English |
Published |
Ostrava
Faculty of Electrical Engineering and Computer Science VSB - Technical University of Ostrava
01.12.2021
VSB-Technical University of Ostrava |
Subjects | |
Online Access | Get full text |
Cover
Loading…
Summary: | Due to the rising usage of various broadcasting systems and web-casting applications, a measurement of audio quality has become an essential task. This paper presents a benchmark of the parametric models for non-intrusive estimation of the audio quality perceived by the end user. The proposed solution is based on machine learning techniques for broadcasting systems and web-casting applications. The main goal of this study is to assess the performance of the non-intrusive parametric models as well as to evaluate a statistical significance of the performance differences between those models. The paper provides a comparison of several models based on the Support Vector Regression, Genetic Programming, Multigene Symbolic Regression, Neural Networks and Random Forest. The obtained results indicate that among the investigated models the most accurate, although not the fastest ones, are the model based on Random Forest (a broadcast scenario) and the SVR-based model (a web-cast scenario). These models represent promising candidates for non-intrusive parametric audio quality assessment in the context of broadcasting systems and web-casting applications. |
---|---|
ISSN: | 1336-1376 1804-3119 |
DOI: | 10.15598/aeee.v19i4.4207 |