Classification for Big Dataset of Bioacoustic Signals Based on Human Scoring System and Artificial Neural Network
Computer Vision and Pattern Recognition
2013-06-19 v2
Abstract
In this paper, we propose a method to improve sound classification performance by combining signal features, derived from the time-frequency spectrogram, with human perception. The method presented herein exploits an artificial neural network (ANN) and learns the signal features based on the human perception knowledge. The proposed method is applied to a large acoustic dataset containing 24 months of nearly continuous recordings. The results show a significant improvement in performance of the detection-classification system; yielding as much as 20% improvement in true positive rate for a given false positive rate.
Keywords
Cite
@article{arxiv.1305.3633,
title = {Classification for Big Dataset of Bioacoustic Signals Based on Human Scoring System and Artificial Neural Network},
author = {Mohammad Pourhomayoun and Peter Dugan and Marian Popescu and Denise Risch and Hal Lewis and Christopher Clark},
journal= {arXiv preprint arXiv:1305.3633},
year = {2013}
}
Comments
To be Submitted to "ICML 2013 Workshop on Machine Learning for Bioacoustics", 6 pages, 4 figures