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Performance Enhancement of OMP Algorithm for Compressed Sensing Based Sparse Channel Estimation in OFDM Systems Read chapter Read first chapter

2018 | OriginalPaper | Chapter

94. Evaluation of Audio Denoising Algorithms for Application of Unmanned Aerial Vehicles in Wildlife Monitoring

Authors : Yun Long Lan, Ahmed Sony Kamal, Carlo Lopez-Tello, Ali Pour Yazdanpanah, Emma E. Regentova, Venkatesan Muthukumar

Published in: Information Technology - New Generations

Publisher: Springer International Publishing

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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.

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Literature
1.
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.
2.
Guoshen, Y., & Mallat, S., & Bacry, E. (2008). Audio denoising by time-frequency block thresholding. IEEE Transactions on Signal Processing, 56(5), 1830–1839.
3.
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).
4.
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).
5.
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).
6.
Mihov, S.G., Ivanov, R.M., & Popov, A. N. (2009). Denoising speech signals by wavelet transform. Annual Journal of Electronics. ISSN:1313–1842.
7.
Rox, J., & Vincent, E. (2013). Consistent wiener filtering for audio source separation. IEEE Signal Processing Letters, 20(3), 217–220.CrossRef
8.
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).
9.
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).
10.
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).
11.
Cai, T. (1999). Adaptive wavelet estimation: A block thresholding and oracle inequality approach. Annals Statistics, 27, 898–924.MathSciNetCrossRefMATH
12.
Cai, T., & Zhouo, H. (2005). A data driven block thresholding approaches to wavelet estimation. Statistics Department, University of Pennsylvania, Technical Report.
13.
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
14.
Donoho, D. L. (1992). Denoising by soft-thresholding. Technical Report no. 409, Stanford University.
15.
Wieland, B., Urban, K., & Funken, S. (2009). Speech signal noise reduction with wavelets. Dissertation, Diplomarbeit an der Universitt Ulm.
Metadata
Title
Evaluation of Audio Denoising Algorithms for Application of Unmanned Aerial Vehicles in Wildlife Monitoring
Authors
Yun Long Lan
Ahmed Sony Kamal
Carlo Lopez-Tello
Ali Pour Yazdanpanah
Emma E. Regentova
Venkatesan Muthukumar
Copyright Year
2018
Book
Information Technology - New Generations

Print ISBN: 978-3-319-54977-4

Electronic ISBN: 978-3-319-54978-1

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-54978-1_94

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