Discrete Wavelet Transform in digital audio signal processing: A case study of programming languages performance analysis

Escola, João Paulo Lemos; Souza, Uender Barbosa de; Brito, Leonardo da Cunha

doi:10.1016/j.compeleceng.2022.108439

Cited by 4 publications

(1 citation statement)

References 22 publications

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“…Rangel-Magdaleno et al [31] use Discrete Wavelet Transform (DWT) in their study to decompose sound signals in detecting the unbalanced blade of a UAV. DWT can be used to extract useful information from a signal, as well as for denoising, compression, and feature extraction [32]. Yaman et al [33] use the Mel-frequency Cepstral Coefficients (MFCC) method for feature extraction of the audio signal in UAV motor's fault detection.…”

Section: Introductionmentioning

confidence: 99%

Comparison of VTOL UAV Battery Level for Propeller Faulty Classification Model

Mohd Sani,

Mohamad Zin,

Mohd Nor

et al. 2024

JOIV : Int. J. Inform. Visualization

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The degradation of batteries in UAVs may result in various problems, such as connectivity troubles, flight delays, and unexpected accidents. Flight safety and reliability are affected by propeller efficiency and performance. This study explores an acoustic-based method to classify propeller faulty conditions in Vertical Take-Off and Landing Unmanned Aerial Vehicles (VTOL UAV). The main objective is to emphasize the difference between classifier models developed using different battery-level flight data. The sound generated by VTOL UAV provides valuable information about the flight performance, essential for effectively monitoring flying conditions and identifying potential faults. This study uses three classification algorithms-Medium Tree (MT), Linear Support Vector Machine (LSVM), and Linear Discriminant (LD), to classify propeller failures of VTOL UAVs. Datasets are collected from three simulated propeller faulty conditions using a wireless microphone connected to a smartphone in an indoor lab environment with a soundproofing mechanism. The Mel Frequency Cepstral Coefficients technique is implemented in MATLAB (R2020a) to extract valuable features from the recorded sound signals. Extracted features from high and low-battery flights are utilized to develop classification models. Classifiers' performance is analyzed to compare the difference between selected models developed using high and low-battery flight data. The accuracy was measured with other samples to test the robustness of classification models. LSVM and MT classification models developed using high-battery flight data produce better accuracy than low-battery flight data in the training and testing phases. LD classification model developed using high-battery flight data produces better accuracy than low-battery flight data in the testing phase only. These results show that battery degradation can affect the performance of the VTOL UAV faulty classification algorithm.

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Section: Introductionmentioning

confidence: 99%