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.Comment: To be Submitted to "ICML 2013 Workshop on Machine Learning for
Bioacoustics", 6 pages, 4 figure