Prediction of Speech Quality Based on Resilient Backpropagation Artificial Neural Network

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F17%3A10236467" target="_blank" >RIV/61989100:27240/17:10236467 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/17:10236467

Výsledek na webu

<a href="https://link.springer.com/article/10.1007/s11277-016-3746-2" target="_blank" >https://link.springer.com/article/10.1007/s11277-016-3746-2</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11277-016-3746-2" target="_blank" >10.1007/s11277-016-3746-2</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Prediction of Speech Quality Based on Resilient Backpropagation Artificial Neural Network

Popis výsledku v původním jazyce

The paper presents a system for monitoring and assessment the speech quality in the IP telephony infrastructures using modular probes. The probes are placed at key nodes in the network where aggregating packet loss data. The system dynamically measures speech quality and results are collected on a central server. For data analysis we applied four-state Markov model for modeling the impact of network impairments on speech quality, afterwards, the resilient back propagation (Rprop) algorithm was used to train a neural network. Information about the speech quality are displayed in the form of automatically generated graphs and tables. The proposed solution has been tested with selected codecs and further generalizes the already presented concepts of the speech quality estimation in the IP environment.

Název v anglickém jazyce

Prediction of Speech Quality Based on Resilient Backpropagation Artificial Neural Network

Popis výsledku anglicky

The paper presents a system for monitoring and assessment the speech quality in the IP telephony infrastructures using modular probes. The probes are placed at key nodes in the network where aggregating packet loss data. The system dynamically measures speech quality and results are collected on a central server. For data analysis we applied four-state Markov model for modeling the impact of network impairments on speech quality, afterwards, the resilient back propagation (Rprop) algorithm was used to train a neural network. Information about the speech quality are displayed in the form of automatically generated graphs and tables. The proposed solution has been tested with selected codecs and further generalizes the already presented concepts of the speech quality estimation in the IP environment.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20203 - Telecommunications

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Wireless Personal Communications

ISSN

0929-6212

e-ISSN

—

Svazek periodika

96

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

15

Strana od-do

5375-5389

Kód UT WoS článku

000411881300025

EID výsledku v databázi Scopus

2-s2.0-84990843631