Animal Sound Analysis and Classification

We propose a robust approach for automatic animal sound classification, addressing challenges in variable sound lengths, diverse frequencies, and noisy data. We introduce innovative techniques like MFCC feature matrix rearrangement, data reduction using autoencoders, and a deep learning model with Bi-LSTM and attention mechanisms. We contribute an animal sound benchmark dataset composed of marine animals and birds.