Acoustic contamination detection using QQ-plot based decision scheme

Contamination detection in acoustic signals is crucial for their applicability in industrial surroundings and for robustification of state estimation algorithms which use them. This paper presents an adaptive solution which estimates nominal signal statistics and detects contamination by measuring the deviation of the obtained signal distribution from the expected one without requiring a state space model that is usually used in existing algorithms. Detecting a change in nominal working condition, this algorithm adapts to it by repeating the training procedure. Statistical deviation detection is conducted using Quantile-Quantile plot (QQ-plot) based decision scheme which calculates metrics in the form of mean squared error of deviation of the recorded signal from the expected distribution. This algorithm has been tested and verified on real acoustic signals obtained in noisy industrial surroundings. The results are compared to a well known T2 multivariate control chart procedure.