Automatic Bird Individual Recognition in Multi-Channel Recording Scenarios

Type: BA thesis

Status: finished

Date: April 1, 2021 - September 1, 2021

Supervisors: Elmar Nöth, Christian Bergler, Dr. rer. nat. Patrick Krauss, UK Erlangen

Problem background:
At the Max-Planck-Institute for Ornithology in Radolfszell several birds are equipped with
backpacks to record their calls. But not only the sound of the equipped bird is recorded but also
of the birds in its surroundings and as a result the scientists receive several non-synchronous
audio tracks with bird calls. The biologists have to manually match the calls to the individual
birds, which is time-consuming and can easily lead to mistakes.
Goal of the thesis:
The goal of this thesis is to implement a python framework that can assign the calls to the
corresponding birds.
Since the intensity of a call decreases exponentially with distance, the loudest call can be
matched to the bird with this recorder. Also, the call of the mentioned bird appears earlier on
its own recording device than on the other devices.
To assign the further calls to the remaining birds, the soundtracks must be compared by
overlaying the audio signals. For this purpose, the audio signals have to be modified first:
Since different devices are used for capturing data and because the recordings cannot be started
at the same time, a linear time offset between the recordings occurs. Also, a linear distortion
appears as the devices record at different frequencies.
To remove these inconsistencies, similar characteristics must be found in the audio signals and
then the audio tracks have to be shifted and processed until these characteristics lie one above
another. There are several methods to filter out these characteristics, whereby the most precise
methods require human assistance [1]. But there are also some automated approaches, where
the audio track is scanned for periodic signal parameters such as pitch or spectral flatness.
Effective features are essential for the removal of distortion as well as a good ability of the
algorithm to distinguish between minor similarities of the characteristics [2].
The framework will be implemented in Python. It should process the given audio tracks and
recognize and reject disturbed channels.
[1] Brett G. Crockett, Michael J. Smithers. Method for time aligning audio signals using
characterizations based on auditory events, 2002
[2] Jürgen Herre, Eric Allamanche, Oliver Hellmuth. Robust matching of audio signals using
spectral flatness features, 2002