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2017
Conference Paper
Titel

Exploring sound source separation for acoustic condition monitoring in industrial scenarios

Abstract
This paper evaluates the application of three methods for Sound Source Separation (SSS) in industrial acoustic condition monitoring scenarios. To evaluate the impact of SSS, we use a machine learning approach where a classifier is trained to detect a specific operating machine. The evaluation procedure is based on simulated and measured data, comprising three different machine sounds as targets and 10 interfering signals. Various intermixing levels of target and interfering signal are taken into account, using three different signal-to-interference ratios. Results show that the chosen source separation methods, originally developed for music analysis, work well for industrial signals, significantly improving the classification accuracy.
Author(s)
Cano, Estefanía
Nowak, Johannes
Grollmisch, Sascha
Hauptwerk
25th European Signal Processing Conference, EUSIPCO 2017
Konferenz
European Signal Processing Conference (EUSIPCO) 2017
Thumbnail Image
DOI
10.23919/EUSIPCO.2017.8081613
Language
English
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