Data Augmentation for Audio Based Machine Learning Classifying Brachycephalic Obstructive Airway Syndrome (BOAS) in Dogs
Typ
Examensarbete för masterexamen
Program
Publicerad
2021
Författare
Pettersson, Henrik
Stensöta, Olivia
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Breathing problems of varying degree are common amongst dog breeds with shorter
snouts also called brachycephalic dogs. The process of classifying each case consists
of a veterinarian visit where tests are preformed to assess the severity on a scale from
zero to three. In this master thesis, we aim to simplify this procedure by machine
learning and will be working with two hypothesis. Hypothesis I is a continuation
of the master thesis Brachycephalic Obstruction Airway Syndrome (BOAS) classification
in dogs based on respiratory noise analysis using machine learning by Moa
Mårtensson. Here we augmented the audio files to generate a larger data set and
extracted multiple features. The features include MFCC, ZCR and RMS that are
fed to a LSTM network. The second hypothesis aims to classify BOAS(-) and (+),
this hypothesis uses frequency data enhanced with SMOTE and a CNN. We show
that it is possible to classify BOAS using machine learning, but that more data is
required in order to confidently diagnose BOAS. We can conclude that hypothesis II
using data collected from the Littmann device shows the best result on unseen audio
files. There is a possibility to further develop this into a tool for both veterinarians
and dog owners.
This thesis is a collaboration between Chalmers University of Technology and the
Swedish University of Agricultural Sciences in Uppsala.
Beskrivning
Ämne/nyckelord
machine learning , augmenting , MFCC , RMS , ZCR , SMOTE , BOAS