Voice Control of Technical Functions in Smart Home with KNX Technology

Vaňuš, Jan; Smolon, Marek; Koziorek, Jiří; Martínek, Radek

doi:10.1007/978-3-662-45402-2_68

Cited by 6 publications

(5 citation statements)

References 5 publications

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“…The proposed method for optimal adjustment of the step size factor µ opt and the filter length of M of the LMS adaptive filter was applied in the following steps [66], [69]- [71]:…”

Section: Lms Af Typementioning

confidence: 99%

Application of a New CO₂ Prediction Method Within Family House Occupancy Monitoring

et al. 2021

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“…The proposed method for optimal adjustment of the step size factor µ opt and the filter length of M of the LMS adaptive filter was applied in the following steps [66], [69]- [71]:…”

Section: Lms Af Typementioning

confidence: 99%

Application of a New CO₂ Prediction Method Within Family House Occupancy Monitoring

et al. 2021

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“…Two other implementations focused on the communication and implementation of a smart home automation system, using a building control standard, the KNX technology. The first used a standalone application running only on a PC with an integrated NLP system [14]. The second presented an implementation of an IBM cloud service, based on a MQTT (Message Queuing Telemetry Transport) broker, which managed to exchange messages between the devices and the user.…”

Section: Related Workmentioning

confidence: 99%

Control of Smart Home Operations Using Natural Language Processing, Voice Recognition and IoT Technologies in a Multi-Tier Architecture

Alexakis

Panagiotakis

Fragkakis

et al. 2019

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The Internet of Things (IoT) is an emerging Internet-based architecture, enabling the exchange of data and services in a global network. With the advent of the Internet of Things, more and more devices are connecting to the Internet in order to help people get and share data or program actions. In this paper, we introduce an IoT Agent, a Web application for monitoring and controlling a smart home remotely. The IoT Agent integrates a chat bot that can understand text or voice commands using natural language processing (NLP). With the use of NLP, home devices are more user-friendly and controlling them is easier, since even when a command or question/command is different from the presets, the system understands the user’s wishes and responds accordingly. Our solution exploits several available Application Programming Interfaces (APIs), namely: the Dialogflow API for the efficient integration of NLP to our IoT system, the Web Speech API for enriching user experience with voice recognition and synthesis features, MQTT (Message Queuing Telemetry Transport) for the lightweight control of actuators and Firebase for dynamic data storage. This is the most significant innovation it brings: the integration of several third-party APIs and open source technologies into one mash-up, highlighting how a new IoT application can be built today using a multi-tier architecture. We believe that such a tiered architecture can be very useful for the rapid development of smart home applications.

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“…where F s is the sampling frequency; see [21] and [22]. The interval in which the maximum peak is searched is derived from the presumed physiological range of T 0 or F 0 , respectively, i.e.…”

Section: Voiced/unvoiced Analysis Of Speech Active Segments 431 Spectral Analysismentioning

confidence: 99%

Improved method of heuristic classification of vowels from an acoustic signal

Krocil¹,

Macháček²,

Koziorek³

et al. 2018

Turk J Elec Eng & Comp Sci

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The aim is to develop an improved method enabling the automatic allocation of vowel symbols to the corresponding time segments of acoustic recordings of an undisturbed speech signal. The combined classification method is based on finding frequencies of the first two local maxims (formants) in a smoothed linear predictive amplitude spectrum (LPC, linear predictive coding) and zero-crossing values of each speech active voiced short-term segment of the recording. Based on these monitored values, simple heuristic conditions are arranged for the classification of the respective vowel. Implementation of the algorithm was realized using the MATLAB environment and its Graphical User Interface (GUI) was used for the user interaction. Verification of the success rate of vowel classification was done using recordings of forty speakers (twenty men and twenty women), where each speaker repeated the vowels repeatedly with short successive pauses. The success rate of recognizing vowels is classified and evaluated based on results obtained from our designed method.

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Voice Control of Technical Functions in Smart Home with KNX Technology

Cited by 6 publications

References 5 publications

Application of a New CO₂ Prediction Method Within Family House Occupancy Monitoring

Application of a New CO₂ Prediction Method Within Family House Occupancy Monitoring

Control of Smart Home Operations Using Natural Language Processing, Voice Recognition and IoT Technologies in a Multi-Tier Architecture

Improved method of heuristic classification of vowels from an acoustic signal

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