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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This research was supported by the Polish National Centre for Research and Development within the grant No. OT4-4B/AGH-PG-WSTKT.
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Kurowski, A., Marciniuk, K., Kostek, B. (2017). Separability Assessment of Selected Types of Vehicle-Associated Noise. In: Zgrzywa, A., Choroś, K., Siemiński, A. (eds) Multimedia and Network Information Systems. Advances in Intelligent Systems and Computing, vol 506. Springer, Cham. https://doi.org/10.1007/978-3-319-43982-2_10
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DOI: https://doi.org/10.1007/978-3-319-43982-2_10
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