Daily sound recognition using a combination of GMM and SVM for home automation

Most elderly people monitoring systems include the detection of abnormal situations, in particular distress situations, as one of their main goals. In order to reach this objective, many solutions end up combining several modalities such as video tracking, fall detection and sound recognition, so as...

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Main Authors: Sehili, M. A., Istrate, D., Dorizzi, B., Boudy, J.
Format: Conference Proceeding
Language:English
Subjects:
Gaussian Mixture Models
Kernel
Noise
Senior citizens
Sound classification
Speaker recognition
Speech
Support vector machines
Vectors
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Istrate, D.
Dorizzi, B.
Boudy, J.
description Most elderly people monitoring systems include the detection of abnormal situations, in particular distress situations, as one of their main goals. In order to reach this objective, many solutions end up combining several modalities such as video tracking, fall detection and sound recognition, so as to increase the reliability of the system. In this work we focus on daily sound recognition as it is one of the most promising modalities. We make a comparison of two standard methods used for speaker recognition and verification: Gaussian Mixture Models (GMM) and Support Vector Machines (SVM). Experimental results show the effectiveness of the combination of GMM and SVM in order to classify sound data sequences when compared to systems based on GMM.
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source IEEE Electronic Library (IEL) Conference Proceedings
subjects Gaussian Mixture Models
Kernel
Noise
Senior citizens
Sound classification
Speaker recognition
Speech
Support vector machines
Vectors
title Daily sound recognition using a combination of GMM and SVM for home automation
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