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The following work outlines an approach for automatic detection andrecognition of periodic pulse train signals using a multi-stage process basedon spectrogram edge detection, energy projection and classification. The methodhas been implemented to automatically detect and recognize pulse train songs ofminke whales. While the long term goal of this work is to properly identify anddetect minke songs from large multi-year datasets, this effort was developedusing sounds off the coast of Massachusetts, in the Stellwagen Bank NationalMarine Sanctuary. The detection methodology is presented and evaluated on 232continuous hours of acoustic recordings and a qualitative analysis of machinelearning classifiers and their performance is described. The trained automaticdetection and classification system is applied to 120 continuous hours,comprised of various challenges such as broadband and narrowband noises, lowSNR, and other pulse train signatures. This automatic system achieves a TPR of63% for FPR of 0.6% (or 0.87 FP/h), at a Precision (PPV) of 84% and an F1 scoreof 71%.

Bioacoustical Periodic Pulse Train Signal Detection and Classification using Spectrogram Intensity Binarization and Energy Projection