@InProceedings{Schneider2020_446,
author = {Maja Schneider and Oliver Jokisch},
booktitle = {Studientexte zur Sprachkommunikation: Elektronische Sprachsignalverarbeitung 2020},
title = {Towards a Robust Analysis and Classification of Dog Barking},
year = {2020},
editor = {Andreas Wendemuth and Ronald Böck and Ingo Siegert},
month = mar,
pages = {117--124},
publisher = {TUDpress, Dresden},
abstract = {The analysis of animal sounds or even communication is an emerging
research topic, e.g. in biodiversity research, climate studies or digital farming.
Considering animal sounds in a natural environment, it becomes clear, that the underlying
signal processing may be quite challenging, e.g. by a low signal-to-noise
ratio due to a large microphone distance or other acoustic peculiarities, e.g. additional
sound sources. Furthermore, the classification of the signals depends on the
availability and interpretability of appropriate (and annotated) sound data, e.g. representative
recordings of dog barking in our contribution. We investigated, whether
specific dog barking can be distinguished from silence or other sounds, like animal
or traffic noise, to control a window-closing mechanism in a smart home scenario.
The sound recordings have been collected and improved with a wavelet de-noising
technique and notch filters. The analysis included varying analysis frames between
21 and 168 ms, and up to 8,239 temporal or spectral features that are reduced to a
set of 51 features by a Linear Discriminant Analysis (LDA). Additionally, we applied
a Correlation-based Feature Selection (CFS) method. We then classified the
samples by various methods, namely AdaBoost, Random Forest, Support Vector
Machine (SVM), Multi-layer Perceptron (MLP) and decision tree C4.5. Preliminary
results show the best performance for a selection of all 51 features (after LDA)
without any CFS, based on analysis frames of 21 ms. The described methods are
useful to detect barking of one specific dog.},
isbn = {978-3-959081-93-1},
issn = {0940-6832},
keywords = {Acoustic Signals},
url = {https://www.essv.de/pdf/2020_117_124.pdf},
}