Evaluation of audio denoising algorithms for application of unmanned aerial vehicles in wildlife monitoring

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.