Abstract
Unmanned Aerial Vehicles (UAVs) have become popular alternative for wildlife monitoring and border surveillance applications. Elimination of the UAV’s background noise for effective classification of the target audio signal is still a major challenge due to background noise of the vehicles and environments and distances to signal sources. The main goal of this work is to explore acoustic denoising algorithms for effective UAV’s background noise removal. Existing denoising algorithms, such as Adaptive Least Mean Square (LMS), Wavelet Denoising, Time-Frequency Block Thresholding, and Wiener Filter, were implemented and their performance evaluated. LMS and DWT algorithms were implemented on a DSP board and their performance compared using software simulations. Experimental results showed that LMS algorithm’s performance is robust compared to other denoising algorithms. Also, required SNR gain for effective classification of the denosied audio signal is demonstrated.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Marmaroli, P., & Falourd, X. (2012). A UAV motor denoising technique to improve localization of surrounding noisy aircrafts: Proof of concept for anti-collision systems. IEEE Acoustics, Speech and Signals, 333–351.
Guoshen, Y., & Mallat, S., & Bacry, E. (2008). Audio denoising by time-frequency block thresholding. IEEE Transactions on Signal Processing, 56(5), 1830–1839.
Kumari, L., Reddy, K., Krishna, H., & Subash, V. (2011). Time-frequency block thresholding approach for audio denoising. International Journal of Advances in Science and Technology, 2(5).
Pour Yazdanpanah, A., Mandava, A. K., Regentova, E. E., Muthukumar, V., & Bebis, G. (2014). A CUDA based implementation of locally-and feature-adaptive diffusion based image denoising algorithm. In 11th International Conference on Information Technology: New Generations (pp. 388–393).
Obulesu, K. P., & Uday Kumar, P. (2013). Implementation of time frequency block thresholding algorithm in audio noise reduction. International Journal of Science, Engineering and Technology Research (IJSETR), 2(7).
Mihov, S.G., Ivanov, R.M., & Popov, A. N. (2009). Denoising speech signals by wavelet transform. Annual Journal of Electronics. ISSN:1313–1842.
Rox, J., & Vincent, E. (2013). Consistent wiener filtering for audio source separation. IEEE Signal Processing Letters, 20(3), 217–220.
Mao, R., Zhou, Y., & Liu, H. (2015). An improved iterative wiener filtering algorithm for speech enhancement. In IEEE International Conference on Computers and Communications (ICCC) (pp. 436–440).
Xu, L., et al. (2010). Research on LMS adaptive filtering algorithm for acoustic emission signal processing. In Proceeding of the 8th World Congress on Intelligent Control and Automation, 6–9 July 2010 (pp. 7037–7040).
Wu, Y., & Zhao, W. (2013). The improvement of audio noise reduction system based on LMS algorithm. In International Conference on Computer Science and Application (pp. 590–594).
Cai, T. (1999). Adaptive wavelet estimation: A block thresholding and oracle inequality approach. Annals Statistics, 27, 898–924.
Cai, T., & Zhouo, H. (2005). A data driven block thresholding approaches to wavelet estimation. Statistics Department, University of Pennsylvania, Technical Report.
Gargour, C., Gabrea, M., Ramachandran, V., & Lina, J. (2009). A short introduction to wavelets and their application. IEEE Circuits and System Magazine, 2, 57–67. ISSN:1531–636X
Donoho, D. L. (1992). Denoising by soft-thresholding. Technical Report no. 409, Stanford University.
Wieland, B., Urban, K., & Funken, S. (2009). Speech signal noise reduction with wavelets. Dissertation, Diplomarbeit an der Universitt Ulm.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Lan, Y.L., Kamal, A.S., Lopez-Tello, C., Yazdanpanah, A.P., Regentova, E.E., Muthukumar, V. (2018). Evaluation of Audio Denoising Algorithms for Application of Unmanned Aerial Vehicles in Wildlife Monitoring. In: Latifi, S. (eds) Information Technology - New Generations. Advances in Intelligent Systems and Computing, vol 558. Springer, Cham. https://doi.org/10.1007/978-3-319-54978-1_94
Download citation
DOI: https://doi.org/10.1007/978-3-319-54978-1_94
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-54977-4
Online ISBN: 978-3-319-54978-1
eBook Packages: EngineeringEngineering (R0)