Comparative analysis of deep learning models for bird song classification.

Identifying bird songs is essential in monitoring birds and their behaviour without physically interacting with them and disturbing their natural habitat. The field of audio classification is a recently emerging area of research that is mostly interpreted using supervised machine learning techniques. In recent studies, image classification methods and algorithms have shown great promise in this field. This paper analyses three different deep-learning models which are ANN, CNN and RNN-LSTM and compares these models comprehensively with the above-mentioned models. Metrics such as precision, recall and F1-score have been taken into consideration to decide which model among the three models is best for audio classification. The audio files are converted into Mel-Spectrogram to obtain MFCCs and are fed into the models. It is observed that RNN-LSTM provides the highest accuracy followed by CNN and ANN respectively. This comprehensive study of existing models will help the researchers to choose the perfect model for audio classification for further research. [ABSTRACT FROM AUTHOR]