Comparative performance analysis of three classifiers for acoustic signal-based recognition of motorcycles using time- and frequency-domain features

Vehicles of different types generate dissimilar sound patterns even in similar working conditions. In this study, the motorcycles are classified into bikes and scooters based on the sounds produced by them. Simple time-domain features and frequency-domain features are used for classifiers. The performances of artificial neural network, knowledge-based classifier and dynamic time warping are compared and reported. All these classifiers have shown more than 90% classification accuracy when trained with minimum 40% of the samples.