Estimating the Perceived Audio Quality Based on Multigene Symbolic Regression for Broadcasting Systems and Web-Casting Applications

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@InProceedings{Jakubik:2021:RADIOELEKTRONIKA,

• author = "Martin Jakubik and Peter Pocta",
• title = "Estimating the Perceived Audio Quality Based on Multigene Symbolic Regression for Broadcasting Systems and Web-Casting Applications",
• booktitle = "2021 31st International Conference Radioelektronika (RADIOELEKTRONIKA)",
• year = "2021",
• month = "19-21 " # apr,
• address = "Brno, Czech Republic",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-1-6654-1474-6",
• DOI = "doi:10.1109/RADIOELEKTRONIKA52220.2021.9420201",
• size = "5 pages",
• abstract = "In these challenging times of pandemic, people are increasingly using various broadcasting systems and webcasting applications. For this reason, the importance of evaluating the perceived quality from the perspective of the end user of these applications is also growing. In this paper we present a design and performance evaluation of parametric models estimating the audio quality perceived by the end users of broadcasting systems and web-casting applications. We used a concept of symbolic regression (SR) by Multi-Gene Genetic Programming (MGGP). Symbolic regression (SR) is used to discover mathematical expressions of functions that are multigene in nature, i.e. linear combinations of the input variables. Multigene symbolic regression was validated as an effective method by the results obtained by the designed parametric audio quality estimation models, providing good accuracy and generalisation capabilities.",
• notes = "Also known as \cite{9420201}",

}

Genetic Programming entries for Martin Jakubik Peter Pocta

Citations