An EfficientNet-Based Ensemble for Bird-Call Recognition with Enhanced Noise Reduction

Birds are an integral part of the biological ecosystem. However, spotting birds is an arduous task than hearing them chirping. Every bird species has a signature song relying on which ornithologists study birds in an ecosystem. Thus, bird-call recognition plays a pivotal role in identifying birds, understanding biological diversity and conserving them. Previously, a few research works have attempted to devise machine learning and deep learning techniques to identify bird-calls. However, most approaches inadequately focused on pre-processing and eliminating background noise from the audio input. In this paper, a fine-tuned EfficientNet-based ensemble model to classify avian species has been proposed with a novel tri-layered noise reduction approach. It has been augmented with a thresholding-based approach which discards the noisy samples altogether. Furthermore, a tri-point trade-off between the accuracy, model depth, and the model parameter has been hypothesized and validated. The proposed methodology surpasses the existing state-of-the-art models yielding an accuracy of 0.97 and an F1-score of 0.95 on the Cornell Birdcall Identification task.