Speech quality assessment based on virtual instrumentation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F18%3A10241636" target="_blank" >RIV/61989100:27240/18:10241636 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.researchgate.net/publication/325750217_Speech_Quality_Assessment_Based_on_Virtual_Instrumentation" target="_blank" >https://www.researchgate.net/publication/325750217_Speech_Quality_Assessment_Based_on_Virtual_Instrumentation</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3177457.3177459" target="_blank" >10.1145/3177457.3177459</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Speech quality assessment based on virtual instrumentation

Popis výsledku v původním jazyce

This paper introduces a program for objective and subjective evaluation of speech quality. Using this environment, a lot of speech recordings and various indoor and outdoor noises were processed. As a subjective speech evaluation method, the Dynamic time warping (DTW) method was selected, with PARCOR coefficients being chosen as symptom vectors. For the filtration of the noise in the recording, adaptive filtering based on LMS and RLS algorithms was used and the performance of the adaptive filtering was assessed. Similarity ranged from 70% to 95% for both algorithms. In terms of signal to noise ratio, the RLS algorithm ranged from 36 dB to 42 dB, while the LMS algorithm only varied from 20 dB to 29 dB. (C) 2018 Association for Computing Machinery.

Název v anglickém jazyce

Speech quality assessment based on virtual instrumentation

Popis výsledku anglicky

This paper introduces a program for objective and subjective evaluation of speech quality. Using this environment, a lot of speech recordings and various indoor and outdoor noises were processed. As a subjective speech evaluation method, the Dynamic time warping (DTW) method was selected, with PARCOR coefficients being chosen as symptom vectors. For the filtration of the noise in the recording, adaptive filtering based on LMS and RLS algorithms was used and the performance of the adaptive filtering was assessed. Similarity ranged from 70% to 95% for both algorithms. In terms of signal to noise ratio, the RLS algorithm ranged from 36 dB to 42 dB, while the LMS algorithm only varied from 20 dB to 29 dB. (C) 2018 Association for Computing Machinery.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

ACM International Conference Proceeding Series 2018

ISBN

978-1-4503-6339-6

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

49-56

Název nakladatele

Association for Computing Machinery

Místo vydání

New York

Místo konání akce

Sydney

Datum konání akce

8. 1. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000457928800010