Abstract: A method of training a diagnostic model for identifying a fault condition in a vehicle system includes receiving a plurality of vehicle fault condition data sets, each being associated with a known fault condition of a vehicle system. The vehicle fault condition data sets each include audio data representing an audio signal generated by a microphone during operation of the vehicle system having the known fault condition; and operating data indicating an operating state of the vehicle system. Each vehicle fault condition data set is processed. A frequency domain representation of the audio signal is generated and analysed to identify at least one fault indicator component corresponding to the known fault condition. The diagnostic model is trained to identify the at least one fault condition in dependence on the identification of the at least one fault indicator component in each vehicle fault condition data set.

Abstract: An audio processing system is configured to process an audio signal representing sound waves originating from at least one vehicle system disposed on a vehicle. The audio processing system includes a controller configured to receive the audio signal from a microphone. The controller processes the audio signal to generate a frequency domain representation of the audio signal. The controller also receives a first operating signal indicating an operating state of a first said vehicle system. The controller decomposes the frequency domain representation of the audio signal in dependence on the first operating signal to identify a first audio profile associated with the operation of the first vehicle system. Aspects of the present invention also relate to a vehicle including an audio processing system.