Adnan Husakovic, Anna Mayrhofer, Eugen Pfann, Mario Huemer, Andreas Gaich, Thomas Kühas,
"Acoustic Monitoring - A Deep LSTM Approach for a Material Transport Process"
: Computer Aided Systems Theory - EUROCAST 2019, Part II, Lecture Notes in Computer Science (LNCS), Serie Lecture Notes in Computer Science (LNCS), Vol. 12014, Springer, Seite(n) 44-51, 4-2020, ISBN: 978-3-030-45096-0
Original Titel:
Acoustic Monitoring - A Deep LSTM Approach for a Material Transport Process
Sprache des Titels:
Englisch
Original Buchtitel:
Computer Aided Systems Theory - EUROCAST 2019, Part II, Lecture Notes in Computer Science (LNCS)
Original Kurzfassung:
Robust classification strongly depends on the combination of properly chosen features and the classification algorithm. This paper investigates an autoencoder for feature fusion together with recurrent neural networks such as the Long Short-Term Memory neural networks (LSTMs) in different configurations applied to a dataset of a material transport process. As an important outcome the investigations show that the application of features acquired from the autoencoder bottleneck layer in combination with a bidirectional LSTM improve the classification algorithm significantly and require fewer features in comparison to standard machine learning algorithms.
Forschungs-