Classification of oils by ECOC based multi-class SVM using spectral analysis of acoustic signals

Abstract Acoustic signals passing through a material gather a great deal of analytical information. Such information act as a signature for classification of the materials. Therefore, the paper proposes a novel method for classification of oils by extracting spectral features from acoustic signals. A developed acoustic resonance spectrometry system has been used for the acquisition of acoustic signals from different oils. The system has a quartz V-tube and two similar piezoelectric transducers, attached at the two ends of the tube. One of the transducer acts as a transmitter and the others as a receiver. The transmitter generates the vibrations with the excitation of white noise. The vibrations transmitted through the tube along with oil from the transmitter end to the receiver end. At the receiver end, vibrations translated into the acoustic signal that has been recorded and analyzed. The experiments were performed on six different oils samples namely: Castor, Mustard, Olive, D.Almond, Petrol, and Tarpin. The Burg spectral analysis was carried out on acoustic signals and eight resonance peaks frequency were recorded. The principal component analysis was done on the collected resonance frequencies datasets. The first three principal components were given as input to ECOC SVM classifier using radial basic function kernel in five different output coding schemes one versus one (1VS1), one versus all (1VSA), ordinal (ORD), and binary complete (BNC). The accuracies of SVM_1VS1, SVM_1VSA, SVM_ORD, and SVM_BNC are found to be 100% in classifying Castor, Mustard, Olive, and Tarpin. The best classifier in classifying D.Almond and Petrol is SVM_ORD that has accuracies of 96.75% and 91.47% respectively. The total classification accuracies of classifiers SVM_1VS1, SVM_1VSA, SVM_ORD, and SVM_BNC are 95.71%, 93.08%, 98.03%, and 95.47% respectively. Hence, the variation in resonant peaks in the Burg autoregressive spectrum of acoustic signals showed significant potential in classifying the oils.