Acoustic Monitoring - A Deep LSTM Approach for a Material Transport Process

Adnan Husakovic; Anna Mayrhofer; Eugen Pfann; Mario Huemer; Andreas Gaich; Thomas Kühas

doi:10.1007/978-3-030-45096-0_6

Acoustic Monitoring - A Deep LSTM Approach for a Material Transport Process

Adnan Husakovic
, Anna Mayrhofer
, Eugen Pfann
, Mario Huemer
, Andreas Gaich
, Thomas Kühas

Institute of Signal Processing

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

Abstract

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.

Original language	English
Title of host publication	Computer Aided Systems Theory - EUROCAST 2019, Part II, Lecture Notes in Computer Science (LNCS)
Editors	Roberto Moreno-Díaz, Alexis Quesada-Arencibia, Franz Pichler
Publisher	Springer
Pages	44-51
Number of pages	8
Volume	12014
ISBN (Electronic)	978-3-030-45096-0
ISBN (Print)	9783030450953
DOIs	https://doi.org/10.1007/978-3-030-45096-0_6
Publication status	Published - Apr 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12014 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Fields of science

202017 Embedded systems
202036 Sensor systems
202 Electrical Engineering, Electronics, Information Engineering
202015 Electronics
202022 Information technology
202027 Mechatronics
202030 Communication engineering
202037 Signal processing

JKU Focus areas

Digital Transformation

Access to Document

10.1007/978-3-030-45096-0_6

Cite this

Husakovic, A., Mayrhofer, A., Pfann, E., Huemer, M., Gaich, A., & Kühas, T. (2020). Acoustic Monitoring - A Deep LSTM Approach for a Material Transport Process. In R. Moreno-Díaz, A. Quesada-Arencibia, & F. Pichler (Eds.), Computer Aided Systems Theory - EUROCAST 2019, Part II, Lecture Notes in Computer Science (LNCS) (Vol. 12014, pp. 44-51). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12014 LNCS). Springer. https://doi.org/10.1007/978-3-030-45096-0_6

Husakovic, Adnan ; Mayrhofer, Anna ; Pfann, Eugen et al. / 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). editor / Roberto Moreno-Díaz ; Alexis Quesada-Arencibia ; Franz Pichler. Vol. 12014 Springer, 2020. pp. 44-51 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Acoustic Monitoring - A Deep LSTM Approach for a Material Transport Process",

abstract = "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.",

author = "Adnan Husakovic and Anna Mayrhofer and Eugen Pfann and Mario Huemer and Andreas Gaich and Thomas K{\"u}has",

year = "2020",

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Husakovic, A, Mayrhofer, A, Pfann, E, Huemer, M, Gaich, A & Kühas, T 2020, Acoustic Monitoring - A Deep LSTM Approach for a Material Transport Process. in R Moreno-Díaz, A Quesada-Arencibia & F Pichler (eds), Computer Aided Systems Theory - EUROCAST 2019, Part II, Lecture Notes in Computer Science (LNCS). vol. 12014, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12014 LNCS, Springer, pp. 44-51. https://doi.org/10.1007/978-3-030-45096-0_6

Acoustic Monitoring - A Deep LSTM Approach for a Material Transport Process. / Husakovic, Adnan; Mayrhofer, Anna; Pfann, Eugen et al.
Computer Aided Systems Theory - EUROCAST 2019, Part II, Lecture Notes in Computer Science (LNCS). ed. / Roberto Moreno-Díaz; Alexis Quesada-Arencibia; Franz Pichler. Vol. 12014 Springer, 2020. p. 44-51 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12014 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

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AB - 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.

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Husakovic A, Mayrhofer A, Pfann E, Huemer M, Gaich A, Kühas T. Acoustic Monitoring - A Deep LSTM Approach for a Material Transport Process. In Moreno-Díaz R, Quesada-Arencibia A, Pichler F, editors, Computer Aided Systems Theory - EUROCAST 2019, Part II, Lecture Notes in Computer Science (LNCS). Vol. 12014. Springer. 2020. p. 44-51. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-45096-0_6

Acoustic Monitoring - A Deep LSTM Approach for a Material Transport Process

Abstract

Publication series

Fields of science

JKU Focus areas

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Other files and links

LCM - Center for Symbiotic Mechatronics - Förderphase 1

Cite this

Acoustic Monitoring - A Deep LSTM Approach for a Material Transport Process

Abstract

Publication series

Fields of science

JKU Focus areas

Access to Document

Other files and links

Projects

LCM - Center for Symbiotic Mechatronics - Förderphase 1

Cite this