2013/05/14 by Marian Popescu, Popescu, Marian, Peter J. Dugan +10 · 1 citation
Biochemistry, Genetics and Molecular Biology · Chemistry · Computer Science · Engineering · #Advanced Chemical Sensor Technologies #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Fractal and DNA sequence analysis #Spectroscopy and Chemometric Analyses #cs.CV
paper · pdf · doi:10.48550/arxiv.1305.3250
ICML 2013 Workshop on Machine Learning for Bioacoustics, 2013, 6 pages
openalex publication_date 2013/05/14 · arxiv created 2013/06/28 · arxiv updated 2013/07/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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%.