Combustion sound classification employing Gaussian Mixture Models

This paper presents a method suitable for the detection of various states of combustion in progress by means of sound analogy analysis. Visual inspection, electro-chemical transducers or analyzing the sound produced during the burning process consist of means by which the quality of the burning process can be assessed. The results may be used when taking decisions with the goal of optimally controlling the combustion process. Classification was performed by using the GMM (Gaussian Mixture Models), the parameters extracted from the recorded sound being the phase parameters and the MFCC (Mel-frequency cepstral coefficients) coefficients. The results prove to be promising and encourage future research in the acoustic relevance in burning quality detection.