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首页> 外文会议>International Conference on Information Technology - New Generations >Evaluation of Audio Denoising Algorithms for Application of Unmanned Aerial Vehicles in Wildlife Monitoring
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Evaluation of Audio Denoising Algorithms for Application of Unmanned Aerial Vehicles in Wildlife Monitoring

机译：野生动物监测中无人航空车辆应用中的音频去噪算法评价

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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.
机译：无人驾驶飞行器（无人机）已成为野生动物监测和边境监测应用的流行替代品。由于车辆和环境的背景噪声以及对信号源的距离，因此消除无人机的背景噪声仍然是由于车辆和环境的背景噪声和信号源的距离导致的主要挑战。这项工作的主要目标是探索声学去噪算法，以实现有效的无人机的背景噪声拆除。实现了现有的去噪算法，例如自适应最小均方（LMS），小波去噪，时频块阈值和维纳滤波器，并评估其性能。使用软件仿真比较DSP板和其性能在DSP板上实现了LMS和DWT算法。实验结果表明，与其他去噪算法相比，LMS算法的性能是坚固的。此外，证明了所需的SNR增益用于有效分类的拒绝音频信号。

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