AI for Earth: Rainforest Conservation by Acoustic Surveillance
This work addresses rainforest conservation for environmentalists and researchers, but it is incremental as it applies existing CNN methods to a new domain-specific dataset.
The paper tackles rainforest conservation by developing convolutional neural network models for environmental sound classification, achieving promising preliminary results on a public audio dataset and a real rainforest sound dataset.
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.