A systematic review of measurements of real-world interior car noise for the “Cadenza” machine-learning project

Abstract

Interior car noise refers to the general noise generated by the engine transmission, the interaction between road and types, and weather conditions such as turbulent wind. For drivers or passengers with hearing loss, these can create especially challenging listening situations. The Cadenza Project is organising a series of machine learning challenges to advance signal processing of music for listeners with a hearing loss, and a key scenario in its first challenge is listening in a car to music in the presence of noise. To create enough machine-learnable training materials we need to simulate typical car noises rather than just use one particular recording. We are systematically reviewing the literature on real-world recordings to determine the range of parameters for these simulations. We searched Web of Science with the terms “(car noise, car noise interior, interior noise) AND (speed OR FFT OR spectr*).” A total of 126 studies have been found so far and 12 papers retained on the basis that a frequency spectrum for interior car noise was provided that was suitable for numerical analysis. Results will be presented.