A Drone Sound Recognition Approach Using Adaptive Feature Fusion and Cross-Attention Feature Enhancement

With the rapid development and widespread application of drones across various fields, drone recognition and classification at medium and long distances have become increasingly important yet challenging tasks. This paper proposes a novel network architecture called AECM-Net, which integrates an adaptive feature fusion (AF) module, an efficient channel attention (ECA), and a criss-cross attention (CCA) mechanism-enhanced multi-scale feature extraction module (MSC). The network employs both Mel-frequency cepstral coefficients (MFCCs) and Gammatone cepstral coefficients (GFCC) as input features, utilizing the AF module to adaptively adjust fusion weights of different feature maps while incorporating ECA channel attention to emphasize key channel features and CCA mechanism to capture long-range dependencies. To validate our approach, we construct a comprehensive dataset containing various drone models within a 50-m range and conduct extensive experiments. The experimental results demonstrate that our proposed AECM-Net achieves superior classification performance with an average accuracy of 95.2% within the 50-m range. These findings suggest that our proposed architecture effectively addresses the challenges of medium and long-range drone acoustic signal recognition through its innovative feature fusion and enhancement mechanisms.