The University of Auckland

Project #20: Localisation of bird calls in NZ bush

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Description:

Have you ever spotted kiwi birds in NZ bush? Can you imagine how many of them are living in a forest? It is actually quite rare that you come across birds like kiwis because of their declining population and nocturnal lifestyle. Therefore it is a quite challenging task for the rangers and researchers to study the ecology of such birds without actually staying in a forest for weeks and months. The DOC (Department of Conservation) has been attempting to identify bird calls from audio recordings collected in a forest however so far this has been done completely manually (hiring people and get them to literally "listen and identify" bird calls) which is not cost effective and the results can also be inaccurate. Recently the DOC called a group of researchers around NZ to develop an automated system to identify bird calls as well as measuring abundance of species. The supervisor is a part of the group in charge of extracting various information from the recording, which is also the scope of this project.
In this project we focus on localising bird calls (i.e. extracting "location" of birds from recordings) using digital signal processing with microphone arrays (i.e. array of more than one microphones). The project will first review the performance of existing signal processing algorithms for sound source localisation then explore a novel localisation algorithm that is more robust against various noise observed in an actual forest. Some field recordings using a newly built recording system would also be conducted in the project.

Type:

Undergraduate

Outcome:

A signal processing algorithm for extracting bird call features from noisy recordings collected in Natinal Parks of NZ
A set of experimental results conducted to verify the performance of the signal processing algorithm using a machine learning algorithm

Prerequisites

This project requires fundamental knowledge in digital signal processing. Students taking this project should have passed MECHENG370 after 2015 otherwise they are expected to enrol in ELECGENG733 as an elective.

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Supervisor

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Lab

Lab allocations have not been finalised