• Journal of the Korean Institute of Gas
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• v.23 no.6
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• pp.97-102
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• 2019

In this study, city gas is the most commonly used in homes, and gas accidents are increasing as demand increases. Among them, accidents due to user carelessness are the most and we try to provide information by establishing scenarios to effectively prevent this. The gas leak in the home is an enclosed space, and the appropriate Gaussian model is applied. As smart home control technology is developed by the 4th industrial revolution, it will make a foundation to provide information through program to predict and prevent gas leakage accidents and apply it to smart home safety platform.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.2
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• pp.817-821
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• 2012

In this paper we applied the optimal mixture number estimation technique in GMM(Gaussian mixture model) using BIC(Bayesian information criterion) and MDL(minimum description length) as a model selection criterion for environmental sounds recognition. In the experiment, we extracted 12 MFCC(mel-frequency cepstral coefficients) features from 9 kinds of environmental sounds which amounts to 27747 data and classified them with GMM. As mentioned above, BIC and MDL is applied to estimate the optimal number of mixtures in each environmental sounds class. According to the experimental results, while the recognition performances are maintained, the computational complexity decreases by 17.8% with BIC and 31.7% with MDL. It shows that the computational complexity reduction by BIC and MDL is effective for environmental sounds recognition using GMM.