Modelling Animal Biodiversity Using Acoustic Monitoring and Deep Learning

by   C. Chalmers, et al.

For centuries researchers have used sound to monitor and study wildlife. Traditionally, conservationists have identified species by ear; however, it is now common to deploy audio recording technology to monitor animal and ecosystem sounds. Animals use sound for communication, mating, navigation and territorial defence. Animal sounds provide valuable information and help conservationists to quantify biodiversity. Acoustic monitoring has grown in popularity due to the availability of diverse sensor types which include camera traps, portable acoustic sensors, passive acoustic sensors, and even smartphones. Passive acoustic sensors are easy to deploy and can be left running for long durations to provide insights on habitat and the sounds made by animals and illegal activity. While this technology brings enormous benefits, the amount of data that is generated makes processing a time-consuming process for conservationists. Consequently, there is interest among conservationists to automatically process acoustic data to help speed up biodiversity assessments. Processing these large data sources and extracting relevant sounds from background noise introduces significant challenges. In this paper we outline an approach for achieving this using state of the art in machine learning to automatically extract features from time-series audio signals and modelling deep learning models to classify different bird species based on the sounds they make. The acquired bird songs are processed using mel-frequency cepstrum (MFC) to extract features which are later classified using a multilayer perceptron (MLP). Our proposed method achieved promising results with 0.74 sensitivity, 0.92 specificity and an accuracy of 0.74.


page 1

page 4

page 6


Our Practice Of Using Machine Learning To Recognize Species By Voice

As the technology is advancing, audio recognition in machine learning is...

ORION-AE: Multisensor acoustic emission datasets reflecting supervised untightening of bolts in a jointed vibrating structure

Monitoring loosening in jointed structures during operation is challengi...

Fish sounds: towards the evaluation of marine acoustic biodiversity through data-driven audio source separation

The marine ecosystem is changing at an alarming rate, exhibiting biodive...

Cetacean Translation Initiative: a roadmap to deciphering the communication of sperm whales

The past decade has witnessed a groundbreaking rise of machine learning ...

Integrating automated acoustic vocalization data and point count surveys for efficient estimation of bird abundance

Monitoring wildlife abundance across space and time is an essential task...

Machine Learning For Distributed Acoustic Sensors, Classic versus Image and Deep Neural Networks Approach

Distributed Acoustic Sensing (DAS) using fiber optic cables is a promisi...