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Voice communication in noisy environments in a Smart House using hybrid LMS plus ICA algorithm

dc.contributor.authorMartinek, Radek
dc.contributor.authorVaňuš, Jan
dc.contributor.authorNedoma, Jan
dc.contributor.authorFridrich, Michael
dc.contributor.authorFrnda, Jaroslav
dc.contributor.authorKawala-Sterniuk, Aleksandra
dc.date.accessioned2021-01-27T12:57:03Z
dc.date.available2021-01-27T12:57:03Z
dc.date.issued2020
dc.identifier.citationSensors. 2020, vol. 20, issue 21, art. no. 6022.cs
dc.identifier.issn1424-8220
dc.identifier.urihttp://hdl.handle.net/10084/142592
dc.description.abstractThis 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.cs
dc.language.isoencs
dc.publisherMDPIcs
dc.relation.ispartofseriesSensorscs
dc.relation.urihttp://doi.org/10.3390/s20216022cs
dc.rights© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectautomatic speech recognitioncs
dc.subjectSmart Home (SH)cs
dc.subjectLabVIEWcs
dc.subjectindependent component analysis (ICA)cs
dc.subjectleast mean squares algorithm (LMS)cs
dc.titleVoice communication in noisy environments in a Smart House using hybrid LMS plus ICA algorithmcs
dc.typearticlecs
dc.identifier.doi10.3390/s20216022
dc.rights.accessopenAccesscs
dc.type.versionpublishedVersioncs
dc.type.statusPeer-reviewedcs
dc.description.sourceWeb of Sciencecs
dc.description.volume20cs
dc.description.issue21cs
dc.description.firstpageart. no. 6022cs
dc.identifier.wos000593544200001


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© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
Kromě případů, kde je uvedeno jinak, licence tohoto záznamu je © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.