  The automatic classification of animal sounds presents an enduring challenge
in bioacoustics, owing to the diverse statistical properties of sound signals,
variations in recording equipment, and prevalent low Signal-to-Noise Ratio
(SNR) conditions. Deep learning models like Convolutional Neural Networks (CNN)
and Long Short-Term Memory (LSTM) have excelled in human speech recognition but
have not been effectively tailored to the intricate nature of animal sounds,
which exhibit substantial diversity even within the same domain. We propose an
automated classification framework applicable to general animal sound
classification. Our approach first optimizes audio features from Mel-frequency
cepstral coefficients (MFCC) including feature rearrangement and feature
reduction. It then uses the optimized features for the deep learning model,
i.e., an attention-based Bidirectional LSTM (Bi-LSTM), to extract deep semantic
features for sound classification. We also contribute an animal sound benchmark
dataset encompassing oceanic animals and birds1. Extensive experimentation with
real-world datasets demonstrates that our approach consistently outperforms
baseline methods by over 25% in precision, recall, and accuracy, promising
advancements in animal sound classification.
