{"title":"Intrinsic mode ensembled statistical cepstral coefficients for feature extraction of ship-radiated noise","authors":"","doi":"10.1016/j.apacoust.2024.110255","DOIUrl":null,"url":null,"abstract":"<div><p>Effective analysis of ship underwater acoustic signals requires accurately capturing and distinguishing subtle differences between various types of signal features. This paper introduces a multi-objective feature extraction method based on intrinsic mode decomposition and statistical parameterized cepstral coefficients, aimed at identifying different ship signals. Firstly, the original sample signals are preprocessed and converted into multiple frame signals. Each acoustic signal frame is then decomposed into intrinsic mode functions (IMFs) using variational mode decomposition (VMD). Cepstral coefficients are extracted from each IMF, and the statistical parameter features of each IMF are integrated to enhance the differentiation of various types of ship-radiated noise. These features also form unique “fingerprints” for each ship type, facilitating identity accurate authentication. The performance of the proposed method is evaluated using both K-nearest neighbors (KNN) and support vector machine (SVM) classification models. Experimental results demonstrate that the synergy between the proposed method and SVM significantly outperforms KNN, effectively distinguishing between 12 types of signals, including 11 ship-radiated signals and background noise, achieving an accuracy rate exceeding 89% across 1000 random tests. This method significantly increases the number of classifiable ship targets, demonstrating its considerable potential in distinguishing various underwater acoustic signals.</p></div>","PeriodicalId":55506,"journal":{"name":"Applied Acoustics","volume":null,"pages":null},"PeriodicalIF":3.4000,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Acoustics","FirstCategoryId":"101","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0003682X24004067","RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ACOUSTICS","Score":null,"Total":0}
引用次数: 0
Abstract
Effective analysis of ship underwater acoustic signals requires accurately capturing and distinguishing subtle differences between various types of signal features. This paper introduces a multi-objective feature extraction method based on intrinsic mode decomposition and statistical parameterized cepstral coefficients, aimed at identifying different ship signals. Firstly, the original sample signals are preprocessed and converted into multiple frame signals. Each acoustic signal frame is then decomposed into intrinsic mode functions (IMFs) using variational mode decomposition (VMD). Cepstral coefficients are extracted from each IMF, and the statistical parameter features of each IMF are integrated to enhance the differentiation of various types of ship-radiated noise. These features also form unique “fingerprints” for each ship type, facilitating identity accurate authentication. The performance of the proposed method is evaluated using both K-nearest neighbors (KNN) and support vector machine (SVM) classification models. Experimental results demonstrate that the synergy between the proposed method and SVM significantly outperforms KNN, effectively distinguishing between 12 types of signals, including 11 ship-radiated signals and background noise, achieving an accuracy rate exceeding 89% across 1000 random tests. This method significantly increases the number of classifiable ship targets, demonstrating its considerable potential in distinguishing various underwater acoustic signals.
期刊介绍:
Since its launch in 1968, Applied Acoustics has been publishing high quality research papers providing state-of-the-art coverage of research findings for engineers and scientists involved in applications of acoustics in the widest sense.
Applied Acoustics looks not only at recent developments in the understanding of acoustics but also at ways of exploiting that understanding. The Journal aims to encourage the exchange of practical experience through publication and in so doing creates a fund of technological information that can be used for solving related problems. The presentation of information in graphical or tabular form is especially encouraged. If a report of a mathematical development is a necessary part of a paper it is important to ensure that it is there only as an integral part of a practical solution to a problem and is supported by data. Applied Acoustics encourages the exchange of practical experience in the following ways: • Complete Papers • Short Technical Notes • Review Articles; and thereby provides a wealth of technological information that can be used to solve related problems.
Manuscripts that address all fields of applications of acoustics ranging from medicine and NDT to the environment and buildings are welcome.