Improving Template-Based Bird Sound Identification

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Title: Improving Template-Based Bird Sound Identification
Author: Lauha, Patrik
Other contributor: Helsingin yliopisto, Matemaattis-luonnontieteellinen tiedekunta
University of Helsinki, Faculty of Science
Helsingfors universitet, Matematisk-naturvetenskapliga fakulteten
Publisher: Helsingin yliopisto
Date: 2021
Language: eng
Thesis level: master's thesis
Degree program: Datatieteen maisteriohjelma
Master's Programme in Data Science
Magisterprogrammet i data science
Specialisation: ei opintosuuntaa
no specialization
ingen studieinriktning
Discipline: none
Abstract: Automatic bird sound recognition has been studied by computer scientists since late 1990s. Various techniques have been exploited, but no general method, that could even nearly match the performance of a human expert, has been developed yet. In this thesis, the subject is approached by reviewing alternative methods for cross-correlation as a similarity measure between two signals in template-based bird sound recognition models. Template-specific binary classification models are fit with different methods and their performance is compared. The contemplated methods are template averaging and procession before applying cross-correlation, use of texture features as additional predictors, and feature extraction through transfer learning with convolutional neural networks. It is shown that the classification performance of template-specific models can be improved by template refinement and utilizing neural networks’ ability to automatically extract relevant features from bird sound spectrograms.
Subject: automated species identification
bird song
convolutional neural networks

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