Design of a machine learning driven helicopter noise suppression system

Abstract Helicopter noise poses significant challenges to pilot health and flight safety. To address this issue, our research team has developed an intelligent noise recognition and suppression system. This system uses optimized sensor arrangements and efficient signal processing methods to accurately extract noise features. The deep learning model achieved an accuracy of 95.8% in noise classification tasks, and ensemble learning strategies further enhanced system robustness. The active noise control component achieved an average suppression effect of 18.7 dB in the 100-500Hz frequency range. The system demonstrated stable and reliable performance under various flight conditions and environmental settings, significantly improving the acoustic environment of the cockpit. This achievement provides technical support for improving helicopter pilot safety and comfort and offers valuable insights for other noise control applications.