Separability Assessment of Selected Types of Vehicle-Associated Noise
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.
Adam Kurowski, Karolina Marciniuk, Bożena Kostek. (2017). Separability Assessment of Selected Types of Vehicle-Associated Noise. Advances In Intelligent Systems And Computing, 506, 113-121. https://doi.org/10.1007/978-3-319-43982-2_10
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