  Saving rainforests is a key to halting adverse climate changes. In this
paper, we introduce an innovative solution built on acoustic surveillance and
machine learning technologies to help rainforest conservation. In particular,
We propose new convolutional neural network (CNN) models for environmental
sound classification and achieved promising preliminary results on two
datasets, including a public audio dataset and our real rainforest sound
dataset. The proposed audio classification models can be easily extended in an
automated machine learning paradigm and integrated in cloud-based services for
real world deployment.
