Bioacoustic Signal Classification Based on Continuous Region Processing, Grid Masking and Artificial Neural Network
This addresses the challenge of bioacoustic monitoring for conservation efforts, but it is incremental as it combines existing techniques for a specific application.
The paper tackles the problem of identifying North Atlantic right whale up-calls in noisy environments by developing a method using continuous region processing, grid masking, and an artificial neural network, achieving an error rate of less than 4.5% false positive rate at a 90% true positive rate.
In this paper, we develop a novel method based on machine-learning and image processing to identify North Atlantic right whale (NARW) up-calls in the presence of high levels of ambient and interfering noise. We apply a continuous region algorithm on the spectrogram to extract the regions of interest, and then use grid masking techniques to generate a small feature set that is then used in an artificial neural network classifier to identify the NARW up-calls. It is shown that the proposed technique is effective in detecting and capturing even very faint up-calls, in the presence of ambient and interfering noises. The method is evaluated on a dataset recorded in Massachusetts Bay, United States. The dataset includes 20000 sound clips for training, and 10000 sound clips for testing. The results show that the proposed technique can achieve an error rate of less than FPR = 4.5% for a 90% true positive rate.