Robust Machine Learning Based Acoustic Classification of a Material Transport Process

Adnan Husakovic; Eugen Pfann; Mario Huemer

doi:10.1109/NEUREL.2018.8587031

Robust Machine Learning Based Acoustic Classification of a Material Transport Process

Adnan Husakovic
, Eugen Pfann
, Mario Huemer

Institute of Signal Processing

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

Abstract

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.

Original language	English
Title of host publication	Proceedings of the 14th Symposium on Neural Networks and Applications (NEUREL 2018)
Number of pages	4
ISBN (Electronic)	9781538669747
DOIs	https://doi.org/10.1109/NEUREL.2018.8587031
Publication status	Published - Nov 2018

Fields of science

202036 Sensor systems
202 Electrical Engineering, Electronics, Information Engineering
202015 Electronics
202022 Information technology
202027 Mechatronics
202037 Signal processing

JKU Focus areas

Computation in Informatics and Mathematics
Mechatronics and Information Processing

Access to Document

10.1109/NEUREL.2018.8587031

Cite this

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title = "Robust Machine Learning Based Acoustic Classification of a Material Transport Process",

abstract = "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.",

author = "Adnan Husakovic and Eugen Pfann and Mario Huemer",

year = "2018",

month = nov,

doi = "10.1109/NEUREL.2018.8587031",

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AU - Pfann, Eugen

AU - Huemer, Mario

PY - 2018/11

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N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/85060926443

U2 - 10.1109/NEUREL.2018.8587031

DO - 10.1109/NEUREL.2018.8587031

M3 - Conference proceedings

SN - 978-1-5386-6974-7

BT - Proceedings of the 14th Symposium on Neural Networks and Applications (NEUREL 2018)

ER -

Robust Machine Learning Based Acoustic Classification of a Material Transport Process

Abstract

Fields of science

JKU Focus areas

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