Abstract
This study proposes a classification method that uses the continuous wavelet transform and the support vector machine approach to classify refrigerant flow noises generated in an air conditioner. The air conditioning noise was identified as an abnormal signal by the use of the first- and second-order moments. The start and end times of refrigerant flow noises were identified by detecting the singularities of the continuous wavelet transform coefficient in the time domain and by means of listening to the measured sounds. Further, the time-frequency characteristics of refrigerant flow noise were analyzed with the continuous wavelet transform. For the support vector machine-based classification of refrigerant flow noise in an air conditioner, the grid search method was used to determine kernel hyperparameters. Five-fold cross validation was employed for the application of the support vector machine to the classification of air conditioner refrigerant noise. In addition, measured sound sources were modified based on classified refrigerant flow noise to compare the classification accuracy of a jury test with the results of the support vector machine.
Highlights
Home appliances have a significant influence on the living conditions of consumers
This study proposed a method for classifying air conditioner refrigerant flow noise based on the support vector machine (SVM) and the continuous wavelet transform (CWT)
The present study proposed a method that uses the CWT and the SVM to classify the refrigerant flow noise generated in air conditioners
Summary
Home appliances have a significant influence on the living conditions of consumers. The vibration of home appliances can cause interior noise in the home and make people uncomfortable. This study proposed a method for classifying air conditioner refrigerant flow noise based on the SVM and the CWT.
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