Robust Machine Learning Based Acoustic Classification of a Material Transport Process

This paper discusses the performance of machine learning classification algorithms based on psychoacoustic features for the monitoring of a material transport process. Reliable and robust classification strongly depends on the proper choice of the feature vector. The method of Principal Component Analysis (PCA) is applied in combination with a classification performance analysis of the individual psycho-acoustic feature types in order to select the best performing features and achieve a feature reduction. The resulting feature subsets are applied to a data set of a material transport process.