A machine learning pipeline for classification of cetacean echolocation clicks in large underwater acoustic datasets.

Kaitlin E Frasier,Holger Klinck

doi:10.1371/journal.pcbi.1009613

Abstract

Machine learning algorithms, including recent advances in deep learning, are promising for tools for detection and classification of broadband high frequency signals in passive acoustic recordings. However, these methods are generally data-hungry and progress has been limited by challenges related to the lack of labeled datasets adequate for training and testing. Large quantities of known and as yet unidentified broadband signal types mingle in marine recordings, with variability introduced by acoustic propagation, source depths and orientations, and interacting signals. Manual classification of these datasets is unmanageable without an in-depth knowledge of the acoustic context of each recording location. A signal classification pipeline is presented which combines unsupervised and supervised learning phases with opportunities for expert oversight to label signals of interest. The method is illustrated with a case study using unsupervised clustering to identify five toothed whale echolocation click types and two anthropogenic signal categories. These categories are used to train a deep network to classify detected signals in either averaged time bins or as individual detections, in two independent datasets. Bin-level classification achieved higher overall precision (>99%) than click-level classification. However, click-level classification had the advantage of providing a label for every signal, and achieved higher overall recall, with overall precision from 92 to 94%. The results suggest that unsupervised learning is a viable solution for efficiently generating the large, representative training sets needed for applications of deep learning in passive acoustics.

Highlights

IntroductionBroadband, impulsive sounds are present in marine environments
Many high frequency, broadband, impulsive sounds are present in marine environments
Manual review of the clusters associated them with two anthropogenic sources, and with five odontocete echolocation click types including: Risso’s dolpin (Grampus griseus), Pacific white-sided (Lagenorhynchus obliquidens; PWS) type A and B, Cuvier’s beaked whale (Ziphius cavirostris) and unidentified delphinids (UD) [28,50] (Fig 8; Table 2)

Summary

Introduction

Broadband, impulsive sounds are present in marine environments. Common sources include echolocating cetaceans, vessels, and echosounders. Recording these underwater sounds is an effective strategy for quantitative autonomous monitoring [1,2], the recordings are acoustically complex and unlabeled. Advances in the longevity, sampling rates and storage capacity of passive acoustic recording technologies facilitate the collection of extremely large datasets across widening frequency bands with high signal density, diversity and event overlap [3]. There is a growing need for detection and classification strategies capable of efficiently analyzing large, varied, unlabeled recording datasets for known and novel signals There is a growing need for detection and classification strategies capable of efficiently analyzing large, varied, unlabeled recording datasets for known and novel signals (e.g. [4]).

Methods

Results

Discussion

Conclusion