Detecting up-calls of Right Whales

The purpose of the study was to develop a machine learning based technique to detect the up-calls of North Atlantic Right Whales from all other noises, like calls of other creatures in the sea, so that ships plying in the seas could be warned of their presence in order to avoid a direct collision with the whales. What made the study quite difficult was the non-stationary component of the signals along with a very low signal to noise ratio. Reduction in the noise content was achieved through a threshold technique based on Steins Unbiased Risk Estimate. To reduce the non-stationary content, the trend and seasonality components of the signals were examined and removed when necessary. This was done in accordance with the Classical Decomposition Theory. In order to find the best features to determine the calls of whales, wavelet packet decomposition technique was used using Daubechies 2 (db2) as mother wavelet. Wavelets were used as they provide a good frequency resolution over other formats like Fourier Transform. This led to the decomposition of the signals into separate filter banks whose energy contents were used as features. A backward sequential feature selection approach then found out the best subset of features to be used for classification. Two classification algorithms, Support Vector Machines and Naive Bayes were used to classify the signals.