Suppressing the Acoustic Effects of UAV Propellers through Deep Learning-Based Active Noise Cancellation 


Vol. 50,  No. 4, pp. 535-548, Apr.  2025
10.7840/kics.2025.50.4.535


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  Abstract

This study presents a deep learning-based Active Noise Cancellation (ANC) system for reducing UAV propeller noise using a Convolutional Neural Network (CNN) model. The proposed system effectively minimizes noise in real-time by extracting key audio features such as amplitude, phase, and frequency components, generating and calculating inverse feature values to construct precise anti-noise signals. This approach enables destructive interference, significantly reducing the propeller noise. The model achieved high-performance metrics, including 94.5% accuracy, 93.2% precision, 96.1% recall, and a loss value of 0.115, demonstrating its efficacy in noise cancellation. Deployed on an Nvidia Jetson NX, the ANC system integrates high-quality microphones and strategically placed speakers on a UAV platform, allowing for real- time noise analysis and anti-noise generation. Indoor and outdoor tests validated a substantial reduction in propeller noise up to 36 dB, highlighting the model’ s robustness and potential for quieter UAV operation in noise-sensitive settings.

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[IEEE Style]

F. A. Khan and S. Y. Shin, "Suppressing the Acoustic Effects of UAV Propellers through Deep Learning-Based Active Noise Cancellation," The Journal of Korean Institute of Communications and Information Sciences, vol. 50, no. 4, pp. 535-548, 2025. DOI: 10.7840/kics.2025.50.4.535.

[ACM Style]

Faisal Ayub Khan and Soo Young Shin. 2025. Suppressing the Acoustic Effects of UAV Propellers through Deep Learning-Based Active Noise Cancellation. The Journal of Korean Institute of Communications and Information Sciences, 50, 4, (2025), 535-548. DOI: 10.7840/kics.2025.50.4.535.

[KICS Style]

Faisal Ayub Khan and Soo Young Shin, "Suppressing the Acoustic Effects of UAV Propellers through Deep Learning-Based Active Noise Cancellation," The Journal of Korean Institute of Communications and Information Sciences, vol. 50, no. 4, pp. 535-548, 4. 2025. (https://doi.org/10.7840/kics.2025.50.4.535)
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pISSN : 1226 - 4717
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