Keywords: adaptive filters, annoying noises, filtering capacity, filtering quality, frequency spectrum, frequency spectrogram, vehicle noise, squeaks, rattles, Kalman filter, RLS filter, recursive least squares, LMS filter, least mean square
Characterisation of adaptive filters used in the identification process of annoying noises in vehicles
To identify annoying noises (squeaks and rattles) inside vehicle cabins, it is necessary to capture the sounds present inside them and eliminate or attenuate all external noises from the obtained signal by using LMS, RLS or Kalman adaptive filters. This work aims to characterise the performance of these filters and select the one most suitable for this application. Analytical and experimental work was developed to characterise the performance of these filters in terms of filtering capacity and filtering quality. Additionally, a subjective evaluation was performed asking a group of users about the filter that best reproduces well-defined noises when they are recorded from moving vehicles. Results showed that the RLS filter is the most suitable. Additionally, it was found that for low frequency input signals all the filters show the lowest filtering capacity.