Abstract
Due to the diversity of ship-radiated noise (SRN), audio segmentation is an essential procedure in the ship statuses/categories identification. However, the existing segmentation methods are not suitable for the SRN because of the lack of prior knowledge. In this paper, by a generalized likelihood ratio (GLR) test on the ordinal pattern distribution (OPD), we proposed a segmentation criterion and introduce it into single change-point detection (SCPD) and multiple change-points detection (MCPD) for SRN. The proposed method is free from the acoustic feature extraction and the corresponding probability distribution estimation. In addition, according to the sequential structure of ordinal patterns, the OPD is efficiently estimated on a series of analysis windows. By comparison with the Bayesian Information Criterion (BIC) based segmentation method, we evaluate the performance of the proposed method on both synthetic signals and real-world SRN. The segmentation results on synthetic signals show that the proposed method estimates the number and location of the change-points more accurately. The classification results on real-world SRN show that our method obtains more distinguishable segments, which verifies its effectiveness in SRN segmentation.
Highlights
To distinguish the statuses/categories of ships according to their radiated noise, we require audio segments to be homogeneous to extract consistent acoustic features
We evaluate the performance of the proposed method on both synthetic signals and real-world ship-radiated noise (SRN), by comparison with the Bayesian Information Criterion (BIC) based segmentation methods [10]
We propose an audio segmentation method for SRN to improve the identification performance of ship statuses/categories
Summary
To distinguish the statuses/categories of ships according to their radiated noise, we require audio segments to be homogeneous to extract consistent acoustic features. Different from the model-based methods, the metric-based methods measure the similarity between two adjacent segments from the statistics of the acoustic features, usually with a three-stage approach: acoustic feature extraction, estimation of the probability distribution, and detection for change-points [6]. The third stage is to establish a criterion for change-point detection, based on the estimated probability distributions of the acoustic features. The performance of the metric-based method mostly depends on the prior knowledge about the signal, such as the distinguishable acoustic features and the probability distribution they follow. Too short window length will give rise to the inconsistent estimations of the acoustic features and their probability distribution, and leads to unreliable change-point detection. We proposed an ordinal pattern distribution (OPD) [19,20] based segmentation method for SRN, to improve the identification performance of ship statuses/categories.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
