In defence and mission-critical communication systems, reliable speech transmission is severely affected by diverse acoustic disturbances such as gunshots, artillery fire, helicopter rotor noise, armored vehicle sound and emergency sirens. Traditional signal processing techniques-like spectral subtraction, Wiener filtering, and classical LMS-based ANC-are limited in handling highly dynamic and non-linear noise environments. These methods assume stationary noise characteristics and often introduc
The proposed system integrates AI/ML-driven noise suppression with adaptive filtering to create a robust ANC pipeline.
In defence and mission-critical communication systems, reliable speech transmission is severely affected by diverse acoustic disturbances such as gunshots, artillery fire, helicopter rotor noise,... Traditional signal processing techniques-like spectral subtraction, Wiener filtering, and classical LMS-based ANC-are limited in handling highly dynamic and non-linear noise environments These methods assume stationary noise characteristics and often introduc
The proposed system integrates AI/ML-driven noise suppression with adaptive filtering to create a robust ANC pipeline. The development begins with dataset generation, where clean speech data is combined with curated defence noise datasets (gunshots, drones, artillery, vehicle engines, wind, etc.) at varying SNR levels. This synthetic data generation ensures coverage of both stationary and impulsive noise scenarios. The training pipeline involves transforming audio into time-frequency representations (e.g., STFT spectrograms) or directly using raw waveform inputs. Models process both full-band and sub-band features to capture global and local dependencies. while its also operates in the complex domain to preserve phase information. Training is performed using loss functions such as SI-SNR,