Automatic classification of selected bird species using LSTM network and attention mechanism based on digital vocalization recordings
DOI:
https://doi.org/10.55225/sti.707Keywords:
bird call recognition, sound classification, neural networks, deep learning, attention mechanism, acoustic signalsAbstract
Aim: This paper focuses on the development and evaluation of a deep learning model designed for the classification of audio signals from 31 bird species. The main objective of the study was to investigate the impact of the Luong attention mechanism on the network’s ability to generalize patterns in sequential data.
Material and methods: A hybrid neural network architecture combining Long Short-Term Memory (LSTM) layers with a global attention mechanism was employed. The data preprocessing pipeline included MFCC (Mel-Frequency Cepstral Coefficients) feature extraction and the alignment of signals to a uniform time interval. The model was subjected to a threefold training and testing procedure on randomly selected data subsets, enabling a reliable assessment of result stability. Classification performance was evaluated using the following metrics: accuracy, F1-score, and mean AUC.
Results and conclusion: The results demonstrated that replacing an additional LSTM layer with an attention mechanism significantly reduces model dimensionality while simultaneously improving classification accuracy and overall performance. The proposed approach achieved a mean accuracy of 0.9787, an F1-score of 0.9493, and a mean AUC value of 0.9991. The combination of LSTM layers with an attention mechanism constitutes an effective tool for the classification of acoustic signals produced by different bird species.
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