Abstract
This study aimed at demonstrating the AED derived feature component significantly outperforming MFCC features or log frequency filter parameters. This is effectively achieved without increasing the number of parameters in acoustic events detection under the speech and other audio sounds recognition. This has been made possible and suitable for the use of AdaBoost approach as discussed in the paper.
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Acknowledgements
This work was supported in part by the Youth Fund of the Sichuan Provincial Education Department under Grant 18ZB0467.
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Wang, M., Zhang, J., Tang, H., Li, Z., Li, J., Wang, Y. (2020). Feature Analysis and Selection in Acoustic Events Detection. In: Xu, Z., Choo, KK., Dehghantanha, A., Parizi, R., Hammoudeh, M. (eds) Cyber Security Intelligence and Analytics. CSIA 2019. Advances in Intelligent Systems and Computing, vol 928. Springer, Cham. https://doi.org/10.1007/978-3-030-15235-2_115
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DOI: https://doi.org/10.1007/978-3-030-15235-2_115
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