Helpdesk

Top image

Editorial board

Darius Andriukaitis
Kaunas University of Technology, Lithuania

Alexander Argyros
The University of Sydney, Australia

Radu Arsinte
Technical University of Cluj Napoca, Romania

Ivan Baronak
Slovak University of Technology, Slovakia

Khosrow Behbehani
The University of Texas at Arlington, United States

Mohamed El Hachemi Benbouzid
University of Brest, France

Dalibor Biolek
University of Defence, Czech Republic

Klara Capova
University of Zilina, Slovakia

Erik Chromy
UPC Broadband Slovakia, Slovakia

Milan Dado
University of Zilina, Slovakia

Petr Drexler
Brno University of Technology, Czech Republic

Eva Gescheidtova
Brno University of Technology, Czech Republic

Ray-Guang Cheng
National Taiwan University of Science and Technology, Taiwan, Province of China

Gokhan Hakki Ilk
Ankara University, Turkey

Janusz Jezewski
Institute of Medical Technology and Equipment, Poland

Rene Kalus
VSB - Technical University of Ostrava, Czech Republic

Ivan Kasik
Academy of Sciences of the Czech Republic, Czech Republic

Jan Kohout
University of Defence, Czech Republic

Ondrej Krejcar
University of Hradec Kralove, Czech Republic

Miroslaw Luft
Technical University of Radom, Poland

Stanislav Marchevsky
Technical University of Kosice, Slovakia

Byung-Seo Kim
Hongik University, Korea

Valeriy Arkhin
Buryat State University, Russia

Nguyen Truong Khang
Van Lang University, Vietnam

Rupak Kharel
University of Huddersfield, United Kingdom

Fayaz Hussain
Ton Duc Thang University, Vietnam

Peppino Fazio
Ca’ Foscari University of Venice, Italy

Fazel Mohammadi
University of New Haven, United States of America

Thang Trung Nguyen
Ton Duc Thang University, Vietnam

Le Anh Vu
Ton Duc Thang University, Vietnam

Miroslav Voznak
VSB - Technical University of Ostrava, Czech Republic

Nguyen Huu Khanh Nhan
Ton Duc Thang University, Vietnam

Zbigniew Leonowicz
Wroclaw University of Science and Technology, Poland

Wasiu Oyewole Popoola
The University of Edinburgh, United Kingdom

Yuriy S. Shmaliy
Guanajuato University, Mexico

Lorand Szabo
Technical University of Cluj Napoca, Romania

Tran Trung Duy
Posts and Telecommunications Institute of Technology, Ho Chi Minh City, Vietnam

Xingwang Li
Henan Polytechnic University, China

Huynh Van Van
Ton Duc Thang University, Vietnam

Lubos Rejfek
University of Pardubice, Czech Republic

Neeta Pandey
Delhi Technological University, India

Huynh The Thien
Ho Chi Minh City University of Technology and Education, Vietnam

Mauro Tropea
DIMES Department of University of Calabria, Italy

Gaojian Huang
Henan Polytechnic University, China

Nguyen Quang Sang
Ho Chi Minh City University of Transport, Vietnam

Anh-Tu Le
Ho Chi Minh City University of Transport, Vietnam

Phu Tran Tin
Ton Duc Thang University, Vietnam


Home Search Mail RSS


A Benchmark of Non-intrusive Parametric Audio Quality Estimation Models for Broadcasting Systems and Web-casting Applications

Martin Jakubik, Peter Pocta

DOI: 10.15598/aeee.v19i4.4207


Abstract

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.

Keywords


Artificial Neural Networks; audio quality estimation; broadcast; machine learning; statistical significance; Support Vector Regression; web-cast.

Full Text:

PDF