An unsupervised Hidden Markov Model-based system for the detection and classification of blue whale vocalizations off Chile

In this paper, we present an automatic method, without human supervision, for the detection and classification of blue whale vocalizations from passive acoustic monitoring (PAM) data using Hidden Markov Model technology implemented with a state-of-the-art machine learning platform, the Kaldi speech...

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Bibliographic Details
Published in:Bioacoustics
Main Authors: Buchan, Susannah J., Mahú, Rodrigo, Wuth, Jorge, Balcazar-Cabrera, Naysa, Gutierrez, Laura, Neira, Sergio, Yoma, Néstor Becerra
Format: Article in Journal/Newspaper
Language:English
Published: Taylor and Francis Ltd. 2020
Subjects:
Blue whale vocalizations
HMM
machine learning
unsupervised detection and classification
Pacific
Patagonia
Blue whale
Online Access:https://doi.org/10.1080/09524622.2018.1563758
https://repositorio.uchile.cl/handle/2250/171568
Description
Summary:In this paper, we present an automatic method, without human supervision, for the detection and classification of blue whale vocalizations from passive acoustic monitoring (PAM) data using Hidden Markov Model technology implemented with a state-of-the-art machine learning platform, the Kaldi speech processing toolkit. 157.5 hours of PAM data were annotated for model training and testing, selected from a dataset collected from the Corcovado Gulf, Chilean Patagonia in 2016. The system obtained produced 85.3% accuracy for detection and classification of a range of different blue whale vocalizations. This system was then validated by comparing its unsupervised detection and classification results with the published results of southeast Pacific blue whale song phrase (‘SEP2’) via spectrogram cross-correlation, involving a dataset collected with a different hydrophone instrument. The proposed system led to a reduction in t

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