Automatic Detection and Compression for Passive Acoustic Monitoring of the African Forest Elephant

02/25/2019
by   Johan Bjorck, et al.
0

In this work, we consider applying machine learning to the analysis and compression of audio signals in the context of monitoring elephants in sub-Saharan Africa. Earth's biodiversity is increasingly under threat by sources of anthropogenic change (e.g. resource extraction, land use change, and climate change) and surveying animal populations is critical for developing conservation strategies. However, manually monitoring tropical forests or deep oceans is intractable. For species that communicate acoustically, researchers have argued for placing audio recorders in the habitats as a cost-effective and non-invasive method, a strategy known as passive acoustic monitoring (PAM). In collaboration with conservation efforts, we construct a large labeled dataset of passive acoustic recordings of the African Forest Elephant via crowdsourcing, compromising thousands of hours of recordings in the wild. Using state-of-the-art techniques in artificial intelligence we improve upon previously proposed methods for passive acoustic monitoring for classification and segmentation. In real-time detection of elephant calls, network bandwidth quickly becomes a bottleneck and efficient ways to compress the data are needed. Most audio compression schemes are aimed at human listeners and are unsuitable for low-frequency elephant calls. To remedy this, we provide a novel end-to-end differentiable method for compression of audio signals that can be adapted to acoustic monitoring of any species and dramatically improves over naive coding strategies.

READ FULL TEXT

page 1

page 2

page 3

page 5

page 8

research
07/11/2023

AnuraSet: A dataset for benchmarking Neotropical anuran calls identification in passive acoustic monitoring

Global change is predicted to induce shifts in anuran acoustic behavior,...
research
08/20/2021

Parsing Birdsong with Deep Audio Embeddings

Monitoring of bird populations has played a vital role in conservation e...
research
01/08/2019

Presence-absence estimation in audio recordings of tropical frog communities

One non-invasive way to study frog communities is by analyzing long-term...
research
12/02/2022

NEAL: An open-source tool for audio annotation

Passive acoustic monitoring is used widely in ecology, biodiversity, and...
research
06/06/2019

GIBBONR: An R package for the detection and classification of acoustic signals using machine learning

1. The recent improvements in recording technology, data storage and bat...
research
11/12/2017

Deep Networks tag the location of bird vocalisations on audio spectrograms

This work focuses on reliable detection and segmentation of bird vocaliz...
research
08/09/2023

Transferable Models for Bioacoustics with Human Language Supervision

Passive acoustic monitoring offers a scalable, non-invasive method for t...

Please sign up or login with your details

Forgot password? Click here to reset