DFEFM: Fusing frequency correlation and mel features for robust edge bird audio detection

Passive acoustic monitoring (PAM) technology is increasingly becoming one of the mainstream methods for bird monitoring. However, detecting bird audio within complex natural acoustic environments using PAM devices remains a significant challenge. To enhance the accuracy (ACC) of bird audio detection (BAD) and reduce both false negatives and false positives, this study proposes a BAD method based on a Dual-Feature Enhancement Fusion Model (DFEFM). This method incorporates per-channel energy normalization (PCEN) to suppress noise in the input audio and utilizes mel-frequency cepstral coefficients (MFCC) and frequency correlation matrices (FCM) as input features. It achieves deep feature-level fusion of MFCC and FCM on the channel dimension through two independent multi-layer convolutional network branches, and further integrates Spatial and Channel Synergistic Attention (SCSA) and Multi-Head Attention (MHA) modules to enhance the fusion effect of the aforementioned two deep features. Experimental results on the DCASE2018 BAD dataset show that our proposed method achieved an ACC of 91.4% and an AUC value of 0.963, with false negative and false positive rates of 11.36% and 7.40%, respectively, surpassing existing methods. The method also demonstrated detection ACC above 92% and AUC values above 0.987 on datasets from three sites of different natural scenes in Beijing. Testing on the NVIDIA Jetson Nano indicated that the method achieved an ACC of 89.48% when processing an average of 10 s of audio, with a response time of only 0.557 s, showing excellent processing efficiency. This study provides an effective method for filtering non-bird vocalization audio in bird vocalization monitoring devices, which helps to save edge storage and information transmission costs, and has significant application value for wild bird monitoring and ecological research. • Boosts bird audio detection in noisy settings via PCEN and merging FCM with MFCC for effective monitoring. • Introduces the DFEFM model using dual-branch CNNs to fuse FCM and MFCC with SCSA and MHA for robust feature integration. • Achieve top performance on DCASE2018 and Beijing datasets, running on NVIDIA Jetson Nano for sustained bird conservation.