AI for Earth: Rainforest Conservation by Acoustic Surveillance

2019/08/20 by Yuan Liu, Liu, Yuan, Zhongwei Cheng +9
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · #Animal Vocal Communication and Behavior #Audio and Speech Processing (eess.AS) #Databases (cs.DB) #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Music and Audio Processing #Sound (cs.SD) #Speech and Audio Processing #cs.DB #cs.LG #cs.SD #eess.AS #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.1908.07517

Accepted to KDD2019 Workshop on Data Mining and AI for Conservation

arxiv created 2019/08/20 · openalex publication_date 2019/08/20 · arxiv updated 2019/08/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

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