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2017 | OriginalPaper | Buchkapitel

Separability Assessment of Selected Types of Vehicle-Associated Noise

verfasst von : Adam Kurowski, Karolina Marciniuk, Bożena Kostek

Erschienen in: Multimedia and Network Information Systems

Verlag: Springer International Publishing

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Abstract

Music Information Retrieval (MIR) area as well as development of speech and environmental information recognition techniques brought various tools intended for recognizing low-level features of acoustic signals based on a set of calculated parameters. In this study, the MIRtoolbox MATLAB tool, designed for music parameter extraction, is used to obtain a vector of parameters to check whether they are suitable for separation of selected types of vehicle-associated noise, i.e.: car, truck and motorcycle. Then, cross-correlation between pairs of parameters is calculated. Parameters for which absolute value of cross-correlation factor is below a selected threshold, are chosen for further analysis. Subsequently, pairs of parameters found in the previous step are analyzed as a graph of low-correlated parameters with the use of the Bron-Kerbosch algorithm. Graph is checked for existence of cliques of parameters linked in all-to-all manner related to their low correlation. The largest clique of low-correlated parameters is then tested for suitability for separation into three vehicle noise classes. Behrens-Fisher statistic is used for this purpose. Results are visualized in the form of 2D and 3D scatter plots.

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Metadaten
Titel
Separability Assessment of Selected Types of Vehicle-Associated Noise
verfasst von
Adam Kurowski
Karolina Marciniuk
Bożena Kostek
Copyright-Jahr
2017
DOI
https://doi.org/10.1007/978-3-319-43982-2_10