An efficient fuzzy based technique for signal classification
Fuzzy systems are currently finding practical applications, ranging from "soft" regulatory control in consumer products to accurate modelling of non-linear systems. This paper presents the design of a classification system for vehicle acoustic signal classification. Traffic management and information systems rely on a suite of sensors for estimating traffic parameters. Currently inductive loop detectors and video-based systems are often used to count and detect vehicles. Loop detectors are expensive to maintain and video-based systems are sensitive to environmental conditions and do not perform well in vehicle classification. Vehicle classification is important in the computation of the percentages of vehicle classes that use streets and motorways. The use of an automated system can lead to adequate road surface maintenance with obvious results in cost and quality. However the sound of a working vehicle could provide an important clue to the vehicle type. A novel approach, based on adaptive fuzzy logic systems, has been discussed in this paper. Its performance is evaluated through a simulation study, using metered data collected from a roadside microphone-array sensor at the Valle d'Aosta highway in north-western Italy. The results indicate that the fuzzy classifier based on the proposed defuzzification method, namely area of balance (AOB), provide more accurate classifications compared to other classifiers.
fuzzy systems vehicle classification neural networks sound signature