Maximum a posteriori Estimation of Noise from Non-Acoustic Reference Signals in Very Low Signal-to-Noise Ratio Environments

This paper examines whether non-acoustic noise reference signals can provide accurate estimates of noise at very low signalto-noise ratios (SNRs) where conventional estimation methods are less effective. The environment chosen for the investigation is Formula 1 motor racing where SNRs are as low as -15dB and the non-acoustic reference signals are engine speed, road speed and throttle measurements. Noise is found to relate closely to these reference signals and a maximum a posteriori method (MAP) is proposed to estimate airflow and tyre noise from these parameters. Objective tests show MAP estimation to be more accurate than a range of conventional noise estimation methods. Subjective listening tests then compare speech enhancement using the proposed MAP estimation to conventional methods with the former found to give significantly higher speech quality.