Abstract
A technique is described which can be used to automatically classify Arctic ambient noise data collected using an array of hydrophones suspended below the ice. The technique is currently designed for analyzing low-frequency (5–60 Hz) noise collected from the central Arctic pack ice. It is capable of distinguishing between nearby or distant active pressure-ridging events, thermal ice-cracking events, biological noise, cable strum, flow noise, and microseisms. Different noise types are distinguished by the power spectra, cross spectral matrices, noise directivity, and modal decomposition of the received sound. The technique is designed to be used in a real-time system which can automatically classify the dominant noise contained in the data over a specified time interval. This technique is applied to data obtained in the Lincoln Sea over 5 days in April 1988 to show how the ambient noise characteristics change with time during this period. Application of the algorithms produced a quantitative classification of the noise type that was clearly recognized in displays of the distinguishing measures.
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