  The following work outlines an approach for automatic detection and
recognition of periodic pulse train signals using a multi-stage process based
on spectrogram edge detection, energy projection and classification. The method
has been implemented to automatically detect and recognize pulse train songs of
minke whales. While the long term goal of this work is to properly identify and
detect minke songs from large multi-year datasets, this effort was developed
using sounds off the coast of Massachusetts, in the Stellwagen Bank National
Marine Sanctuary. The detection methodology is presented and evaluated on 232
continuous hours of acoustic recordings and a qualitative analysis of machine
learning classifiers and their performance is described. The trained automatic
detection and classification system is applied to 120 continuous hours,
comprised of various challenges such as broadband and narrowband noises, low
SNR, and other pulse train signatures. This automatic system achieves a TPR of
63% for FPR of 0.6% (or 0.87 FP/h), at a Precision (PPV) of 84% and an F1 score
of 71%.
