Voice Communication in Noisy Environments in a Smart House Using Hybrid LMS plus ICA Algorithm
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27120%2F20%3A10248315" target="_blank" >RIV/61989100:27120/20:10248315 - isvavai.cz</a>
Alternative codes found
RIV/61989100:27240/20:10248315
Result on the web
<a href="https://www.mdpi.com/1424-8220/20/21/6022" target="_blank" >https://www.mdpi.com/1424-8220/20/21/6022</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/s20216022" target="_blank" >10.3390/s20216022</a>
Alternative languages
Result language
angličtina
Original language name
Voice Communication in Noisy Environments in a Smart House Using Hybrid LMS plus ICA Algorithm
Original language description
This publication describes an innovative approach to voice control of operational and technical functions in a real Smart Home (SH) environment, where, for voice control within SH, it is necessary to provide robust technological systems for building automation and for technology visualization, software for recognition of individual voice commands, and a robust system for additive noise canceling. The KNX technology for building automation is used and described in the article. The LabVIEW SW tool is used for visualization, data connectivity to the speech recognizer, connection to the sound card, and the actual mathematical calculations within additive noise canceling. For the actual recognition of commands, the SW tool for recognition within the Microsoft Windows OS is used. In the article, the least mean squares algorithm (LMS) and independent component analysis (ICA) are used for additive noise canceling from the speech signal measured in a real SH environment. Within the proposed experiments, the success rate of voice command recognition for different types of additive interference (television, vacuum cleaner, washing machine, dishwasher, and fan) in the real SH environment was compared. The recognition success rate was greater than 95% for the selected experiments.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20201 - Electrical and electronic engineering
Result continuities
Project
<a href="/en/project/EF17_049%2F0008425" target="_blank" >EF17_049/0008425: A Research Platform focused on Industry 4.0 and Robotics in Ostrava Agglomeration</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Sensors
ISSN
1424-3210
e-ISSN
1424-8220
Volume of the periodical
20
Issue of the periodical within the volume
21
Country of publishing house
CH - SWITZERLAND
Number of pages
24
Pages from-to
nestrankovano
UT code for WoS article
000593544200001
EID of the result in the Scopus database
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