Adnan Husakovic, Eugen Pfann, Mario Huemer,
"Robust Machine Learning Based Acoustic Classification of a Material Transport Process"
: Proceedings of the 14th Symposium on Neural Networks and Applications (NEUREL 2018), 11-2018, ISBN: 978-1-5386-6974-7
Original Titel:
Robust Machine Learning Based Acoustic Classification of a Material Transport Process
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings of the 14th Symposium on Neural Networks and Applications (NEUREL 2018)
Original Kurzfassung:
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.