scholarly journals Audio Feature Engineering for Occupancy and Activity Estimation in Smart Buildings

Electronics ◽  
2021 ◽  
Vol 10 (21) ◽  
pp. 2599
Author(s):  
Gabriela Santiago ◽  
Marvin Jiménez ◽  
Jose Aguilar ◽  
Edwin Montoya
Keyword(s):  
Speech Recognition ◽  
Smart Environments ◽  
Feature Engineering ◽  
Smart Buildings ◽  
The Past ◽  
Audio Feature ◽  
Occupancy Estimation ◽  
Audio Information ◽  
Activity Estimation

The occupancy and activity estimation are fields that have been severally researched in the past few years. However, the different techniques used include a mixture of atmospheric features such as humidity and temperature, many devices such as cameras and audio sensors, or they are limited to speech recognition. In this work is proposed that the occupancy and activity can be estimated only from the audio information using an automatic approach of audio feature engineering to extract, analyze and select descriptors/variables. This scheme of extraction of audio descriptors is used to determine the occupation and activity in specific smart environments, such that our approach can differentiate between academic, administrative or commercial environments. Our approach from the audio feature engineering is compared to previous similar works on occupancy estimation and/or activity estimation in smart buildings (most of them including other features, such as atmospherics and visuals). In general, the results obtained are very encouraging compared to previous studies.

scholarly journals Smart Built Environment Including Smart Home, Smart Building and Smart City: Definitions and Applied Technologies

2021 ◽  
Author(s):  
Reyhaneh Karimi ◽  
Leila Farahzadi ◽  
Samad M.E. Sepasgozar ◽  
Sharifeh Sargolzaei ◽  
Sanee M. Ebrahimzadeh Sepasgozar ◽  
...  
Keyword(s):  
Built Environment ◽  
Smart Home ◽  
Smart Cities ◽  
Management Systems ◽  
Comprehensive Overview ◽  
Smart Buildings ◽  
Smart Systems ◽  
Smart Building ◽  
Energy Management Systems ◽  
The Past

Technology, particularly over the past decades, has affected the cities and their components, such as building sectors. Consequently, smart building that has currently utilized various technologies which is incorporated into buildings is the core of the present chapter. It provides a comprehensive overview on smart cities, smart buildings and smart home to address what systems and technologies have been incorporated so far. The aim is to review the smart concepts in built environment with the main focus on smart cities, smart buildings, and smart homes. State-of-the-art and current practices in smart buildings were also reviewed to enlighten a set of directions for future studies. The Chapter is primarily focuses on 51 articles in smart buildings/homes, as per collected from various datasets. It represents a summary of systems utilized and incorporared into smart buildings and homes over the past decade (2010–2020). Additional to different features of smart buildings and homes, is the discussion around various fields and system performances currently utilized in smart buildings/homes. Limitations and future trends and directions is also discussed. In total, such building/home systems were categorized into 6 groups, including: security systems, healthcare systems, energy management systems, building/home management systems, automation systems, and activity/movement recognition systems. Furthermore, there are a number of surveys which investigated the user’s acceptance and adoption of the new smart systems in homes and buildings, as presented and summarized thereafter in Tables. The present Chapter is a contribution to a better understanding of the functions and performances of such buildings/homes for further implementation and enhancement so that varying demands of smart citizens are fulfilled and eventually contribute to the development of smart cities.

scholarly journals Keyword Recognition Device Cloud Based

2021 ◽  
Vol 9 (VIII) ◽  
pp. 87-89
Author(s):  
Apurv Singh Yadav
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Speech Recognition ◽  
Internet Of Things ◽  
Recognition System ◽  
The Internet ◽  
Speech Recognition System ◽  
The Past ◽  
Use Of The Internet ◽  
The Internet Of Things

Over the past few decades speech recognition has been researched and developed tremendously. However in the past few years use of the Internet of things has been significantly increased and with it the essence of efficient speech recognition is beneficial more than ever. With the significant improvement in Machine Learning and Deep learning, speech recognition has become more efficient and applicable. This paper focuses on developing an efficient Speech recognition system using Deep Learning.

Designing learned CO2-based occupancy estimation in smart buildings

2018 ◽  
Vol 8 (6) ◽  
pp. 249-255 ◽  
Author(s):  
Colin Brennan ◽  
Graham W. Taylor ◽  
Petros Spachos
Keyword(s):  
Smart Buildings ◽  
Occupancy Estimation

scholarly journals A review of deep-neural automated essay scoring models

2021 ◽  
Author(s):  
Masaki Uto
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Main Idea ◽  
Feature Engineering ◽  
Automated Essay Scoring ◽  
Comprehensive Review ◽  
The Past ◽  
Comprehensive Survey ◽  
Essay Scoring ◽  
Different Characteristics

AbstractAutomated essay scoring (AES) is the task of automatically assigning scores to essays as an alternative to grading by humans. Although traditional AES models typically rely on manually designed features, deep neural network (DNN)-based AES models that obviate the need for feature engineering have recently attracted increased attention. Various DNN-AES models with different characteristics have been proposed over the past few years. To our knowledge, however, no study has provided a comprehensive review of DNN-AES models while introducing each model in detail. Therefore, this review presents a comprehensive survey of DNN-AES models, describing the main idea and detailed architecture of each model. We classify the AES task into four types and introduce existing DNN-AES models according to this classification.

scholarly journals Automatic Speech Recognition in Noise for Parkinson's Disease: A Pilot Study

2021 ◽  
Vol 4 ◽  
Author(s):  
Alireza Goudarzi ◽  
Gemma Moya-Galé
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Speech Recognition ◽  
Pilot Study ◽  
Automatic Speech Recognition ◽  
Speech Intelligibility ◽  
Real Life ◽  
The Past ◽  
Noisy Signals ◽  
Speech Recognition In Noise

The sophistication of artificial intelligence (AI) technologies has significantly advanced in the past decade. However, the observed unpredictability and variability of AI behavior in noisy signals is still underexplored and represents a challenge when trying to generalize AI behavior to real-life environments, especially for people with a speech disorder, who already experience reduced speech intelligibility. In the context of developing assistive technology for people with Parkinson's disease using automatic speech recognition (ASR), this pilot study reports on the performance of Google Cloud speech-to-text technology with dysarthric and healthy speech in the presence of multi-talker babble noise at different intensity levels. Despite sensitivities and shortcomings, it is possible to control the performance of these systems with current tools in order to measure speech intelligibility in real-life conditions.

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