[MARMAM] New publication in Bioacoustics, The International Journal of Animal Sound and its Recording

nbecerra nbecerra at ing.uchile.cl
Mon Feb 11 08:09:20 PST 2019

Dear MARMAM Readers,

We are pleased to share the recent publication of the following paper in 

Susannah J. Buchan,Rodrigo Mahú,Jorge Wuth,Naysa Balcazar-Cabrera,Laura 
Gutierrez,Sergio Neira &Néstor Becerra Yoma. "An unsupervised Hidden 
Markov Model-based system for the detection and classification of blue 
whale vocalizations off Chile". Bioacoustics, Published on line: January 
15th, 2019. https://doi.org/10.1080/09524622.2018.1563758


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 the root mean square error 
relative to published results as high as 80% when compared with 
comparable methods employed elsewhere. This is a significant step in 
advancing the monitoring of endangered whale populations in this region, 
which remains poorly covered in terms of PAM and general ocean 
observation. With further training, testing and validation, this system 
can be applied to other target signals and regions of the world ocean.

Keywords: blue whale vocalizations, unsupervised detection and 
classification, HMM, machine learning.

For any further information, please contact corresponding author:
Prof. Nestor Necerra Yoma at  nbecerra at ing.uchile.cl or 
nbecerray at gmail.com

Thank you and regards,


Néstor Becerra Yoma, Ph.D.
Speech Processing and Transmission Lab
Department of Electrical Engineering
Universidad de Chile
Av. Tupper 2007, POBox 412-3
Santiago, Chile

Tel. +56 2 2 978 4205
E-mail: nbecerra at ing.uchile.cl

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