Bioacoustic Signal Classification Based on Continuous Region Processing, Grid Masking and Artificial Neural Network
In this paper, we develop a novel method based on machine-learning and image processing to identify North Atlantic right whale (NARW) up-calls in the presence of high levels of ambient and interfering noise. We apply a continuous region algorithm on the spectrogram to extract the regions of interest...
Main Authors: | , , , |
---|---|
Format: | Report |
Language: | unknown |
Published: |
arXiv
2013
|
Subjects: | |
Online Access: | https://dx.doi.org/10.48550/arxiv.1305.3635 https://arxiv.org/abs/1305.3635 |
id |
ftdatacite:10.48550/arxiv.1305.3635 |
---|---|
record_format |
openpolar |
spelling |
ftdatacite:10.48550/arxiv.1305.3635 2023-05-15T17:33:03+02:00 Bioacoustic Signal Classification Based on Continuous Region Processing, Grid Masking and Artificial Neural Network Pourhomayoun, Mohammad Dugan, Peter Popescu, Marian Clark, Christopher 2013 https://dx.doi.org/10.48550/arxiv.1305.3635 https://arxiv.org/abs/1305.3635 unknown arXiv arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ Computer Vision and Pattern Recognition cs.CV FOS Computer and information sciences Preprint Article article CreativeWork 2013 ftdatacite https://doi.org/10.48550/arxiv.1305.3635 2022-04-01T13:39:17Z In this paper, we develop a novel method based on machine-learning and image processing to identify North Atlantic right whale (NARW) up-calls in the presence of high levels of ambient and interfering noise. We apply a continuous region algorithm on the spectrogram to extract the regions of interest, and then use grid masking techniques to generate a small feature set that is then used in an artificial neural network classifier to identify the NARW up-calls. It is shown that the proposed technique is effective in detecting and capturing even very faint up-calls, in the presence of ambient and interfering noises. The method is evaluated on a dataset recorded in Massachusetts Bay, United States. The dataset includes 20000 sound clips for training, and 10000 sound clips for testing. The results show that the proposed technique can achieve an error rate of less than FPR = 4.5% for a 90% true positive rate. : To be Submitted to "ICML 2013 Workshop on Machine Learning for Bioacoustics", 6 pages, 8 figures Report North Atlantic North Atlantic right whale DataCite Metadata Store (German National Library of Science and Technology) |
institution |
Open Polar |
collection |
DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
language |
unknown |
topic |
Computer Vision and Pattern Recognition cs.CV FOS Computer and information sciences |
spellingShingle |
Computer Vision and Pattern Recognition cs.CV FOS Computer and information sciences Pourhomayoun, Mohammad Dugan, Peter Popescu, Marian Clark, Christopher Bioacoustic Signal Classification Based on Continuous Region Processing, Grid Masking and Artificial Neural Network |
topic_facet |
Computer Vision and Pattern Recognition cs.CV FOS Computer and information sciences |
description |
In this paper, we develop a novel method based on machine-learning and image processing to identify North Atlantic right whale (NARW) up-calls in the presence of high levels of ambient and interfering noise. We apply a continuous region algorithm on the spectrogram to extract the regions of interest, and then use grid masking techniques to generate a small feature set that is then used in an artificial neural network classifier to identify the NARW up-calls. It is shown that the proposed technique is effective in detecting and capturing even very faint up-calls, in the presence of ambient and interfering noises. The method is evaluated on a dataset recorded in Massachusetts Bay, United States. The dataset includes 20000 sound clips for training, and 10000 sound clips for testing. The results show that the proposed technique can achieve an error rate of less than FPR = 4.5% for a 90% true positive rate. : To be Submitted to "ICML 2013 Workshop on Machine Learning for Bioacoustics", 6 pages, 8 figures |
format |
Report |
author |
Pourhomayoun, Mohammad Dugan, Peter Popescu, Marian Clark, Christopher |
author_facet |
Pourhomayoun, Mohammad Dugan, Peter Popescu, Marian Clark, Christopher |
author_sort |
Pourhomayoun, Mohammad |
title |
Bioacoustic Signal Classification Based on Continuous Region Processing, Grid Masking and Artificial Neural Network |
title_short |
Bioacoustic Signal Classification Based on Continuous Region Processing, Grid Masking and Artificial Neural Network |
title_full |
Bioacoustic Signal Classification Based on Continuous Region Processing, Grid Masking and Artificial Neural Network |
title_fullStr |
Bioacoustic Signal Classification Based on Continuous Region Processing, Grid Masking and Artificial Neural Network |
title_full_unstemmed |
Bioacoustic Signal Classification Based on Continuous Region Processing, Grid Masking and Artificial Neural Network |
title_sort |
bioacoustic signal classification based on continuous region processing, grid masking and artificial neural network |
publisher |
arXiv |
publishDate |
2013 |
url |
https://dx.doi.org/10.48550/arxiv.1305.3635 https://arxiv.org/abs/1305.3635 |
genre |
North Atlantic North Atlantic right whale |
genre_facet |
North Atlantic North Atlantic right whale |
op_rights |
arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
op_doi |
https://doi.org/10.48550/arxiv.1305.3635 |
_version_ |
1766131430421692416 |