Enhancing smart home environments: a novel pattern recognition approach to ambient acoustic event detection and localization
IntroductionAmbient acoustic detection and localization play a vital role in identifying events and their origins from acoustic data. This study aimed to establish a comprehensive framework for classifying activities in home environments to detect emergency events and transmit emergency signals. Loc...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Big Data |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fdata.2024.1419562/full |
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| author | Ahsan Shabbir Abdul Haleem Butt Taha Khan Lorenzo Chiari Ahmad Almadhor Vincent Karovic |
| author_facet | Ahsan Shabbir Abdul Haleem Butt Taha Khan Lorenzo Chiari Ahmad Almadhor Vincent Karovic |
| author_sort | Ahsan Shabbir |
| collection | DOAJ |
| description | IntroductionAmbient acoustic detection and localization play a vital role in identifying events and their origins from acoustic data. This study aimed to establish a comprehensive framework for classifying activities in home environments to detect emergency events and transmit emergency signals. Localization enhances the detection of the acoustic event's location, thereby improving the effectiveness of emergency services, situational awareness, and response times.MethodsAcoustic data were collected from a home environment using six strategically placed microphones in a bedroom, kitchen, restroom, and corridor. A total of 512 audio samples were recorded from 11 activities. Background noise was eliminated using a filtering technique. State-of-the-art features were extracted from the time domain, frequency domain, time frequency domain, and cepstral domain to develop efficient detection and localization frameworks. Random forest and linear discriminant analysis classifiers were employed for event detection, while the estimation signal parameters through rational-in-variance techniques (ESPRIT) algorithm was used for sound source localization.ResultsThe study achieved high detection accuracy, with random forest and linear discriminant analysis classifiers attaining 95% and 87%, respectively, for event detection. For sound source localization, the proposed framework demonstrated significant performance, with an error rate of 3.61, a mean squared error (MSE) of 14.98, and a root mean squared error (RMSE) of 3.87.DiscussionThe integration of detection and localization models facilitated the identification of emergency activities and the transmission of notifications via electronic mail. The results highlight the potential of the proposed methodology to develop a real-time emergency alert system for domestic environments. |
| format | Article |
| id | doaj-art-54116cb1af36459eaa7a3595872c319f |
| institution | Kabale University |
| issn | 2624-909X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Big Data |
| spelling | doaj-art-54116cb1af36459eaa7a3595872c319f2025-01-23T06:56:31ZengFrontiers Media S.A.Frontiers in Big Data2624-909X2025-01-01710.3389/fdata.2024.14195621419562Enhancing smart home environments: a novel pattern recognition approach to ambient acoustic event detection and localizationAhsan Shabbir0Abdul Haleem Butt1Taha Khan2Lorenzo Chiari3Ahmad Almadhor4Vincent Karovic5Department of Creative Technologies, Faculty of Computing and Artificial Intelligence, Air University, Islamabad, PakistanDepartment of Creative Technologies, Faculty of Computing and Artificial Intelligence, Air University, Islamabad, PakistanResearch and Development Department, Detectivio AB, Göteborg, SwedenDepartment of Electrical, Electronic and Information Engineering “Guglielmo Marconi, ” University of Bologna, Bologna, ItalyDepartment of Computer Engineering and Networks, College of Computer and Information Sciences, Jouf University, Sakaka, Saudi ArabiaDepartment of Information Management and Business Systems, Faculty of Management, Comenius University Bratislava, Bratislava, SlovakiaIntroductionAmbient acoustic detection and localization play a vital role in identifying events and their origins from acoustic data. This study aimed to establish a comprehensive framework for classifying activities in home environments to detect emergency events and transmit emergency signals. Localization enhances the detection of the acoustic event's location, thereby improving the effectiveness of emergency services, situational awareness, and response times.MethodsAcoustic data were collected from a home environment using six strategically placed microphones in a bedroom, kitchen, restroom, and corridor. A total of 512 audio samples were recorded from 11 activities. Background noise was eliminated using a filtering technique. State-of-the-art features were extracted from the time domain, frequency domain, time frequency domain, and cepstral domain to develop efficient detection and localization frameworks. Random forest and linear discriminant analysis classifiers were employed for event detection, while the estimation signal parameters through rational-in-variance techniques (ESPRIT) algorithm was used for sound source localization.ResultsThe study achieved high detection accuracy, with random forest and linear discriminant analysis classifiers attaining 95% and 87%, respectively, for event detection. For sound source localization, the proposed framework demonstrated significant performance, with an error rate of 3.61, a mean squared error (MSE) of 14.98, and a root mean squared error (RMSE) of 3.87.DiscussionThe integration of detection and localization models facilitated the identification of emergency activities and the transmission of notifications via electronic mail. The results highlight the potential of the proposed methodology to develop a real-time emergency alert system for domestic environments.https://www.frontiersin.org/articles/10.3389/fdata.2024.1419562/fullambient acoustic analysissound event detectionautonomous monitoringmachine learningdeep learningESPRIT |
| spellingShingle | Ahsan Shabbir Abdul Haleem Butt Taha Khan Lorenzo Chiari Ahmad Almadhor Vincent Karovic Enhancing smart home environments: a novel pattern recognition approach to ambient acoustic event detection and localization Frontiers in Big Data ambient acoustic analysis sound event detection autonomous monitoring machine learning deep learning ESPRIT |
| title | Enhancing smart home environments: a novel pattern recognition approach to ambient acoustic event detection and localization |
| title_full | Enhancing smart home environments: a novel pattern recognition approach to ambient acoustic event detection and localization |
| title_fullStr | Enhancing smart home environments: a novel pattern recognition approach to ambient acoustic event detection and localization |
| title_full_unstemmed | Enhancing smart home environments: a novel pattern recognition approach to ambient acoustic event detection and localization |
| title_short | Enhancing smart home environments: a novel pattern recognition approach to ambient acoustic event detection and localization |
| title_sort | enhancing smart home environments a novel pattern recognition approach to ambient acoustic event detection and localization |
| topic | ambient acoustic analysis sound event detection autonomous monitoring machine learning deep learning ESPRIT |
| url | https://www.frontiersin.org/articles/10.3389/fdata.2024.1419562/full |
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