Signal detection in underwater sound using wavelets

• 成果类型: Article
• 署名作者: Bailey, TC; Sapatinas, T; Powell, KJ; Krzanowski, WJ
• 署名单位: University of Exeter; University of Bristol
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.2307/2669604
• 发表日期: 1998
• 页码: 73-83
• 关键词: orthonormal bases; Cross-validation; CLASSIFICATION; shrinkage

摘要

This article considers the use of wavelet methods in relation to a common signal processing problem, that of detecting transient features in sound recordings that contain interference or distortion. In this particular case, the data are various types of underwater sounds, and the objective is to detect intermittent departures (potential signals) from the background sound environment in the data (noise), where the latter may itself be evolving and changing over time. We develop an adaptive model of the background interference, using recursive density estimation of the joint distribution of certain summary features of its wavelet decomposition. Observations considered to be outliers from this density estimate at any time are then flagged as potential signals. The performance of our method is illustrated on artificial data, where a known signal is contaminated with simulated underwater noise using a range of different signal-to-noise ratios, and a baseline comparison is made with results obtained from a relatively unsophisticated, but commonly used, time-frequency approach. A similar comparison is then reported in relation to the more significant problem of detecting various types of dolphin sound in real conditions.