Snapping shrimp noise detection methods based on linear prediction analysis

Abstract

This paper proposes features for detecting snapping shrimp noise based on linear predictive analysis. Snapping shrimps are a species inhabiting the ocean depths and are one of the main sources of underwater noise due to their high-amplitude signals that occur frequently. The proposed features utilize the characteristic of sudden onset and rapid decay of pistol shrimp noise by using linear predictive analysis to accurately detect the noise segments and mitigate the impact of snapping shrimp noise. The large error between the predicted values by linear predictive analysis and the actual measured values allows for effective detection of pistol shrimp segments. Additionally, the proposed features are combined with a fixed-threshold detector to further improve the performance of noise segment detection. When compared to the state-of-the-art method known as multilayer wavelet packet decomposition, the proposed method outperformed with an average improvement of 0.12 in terms of receiver operating characteristic curve and area under the curve. Moreover, the proposed method had lower computational complexity.