DEMON-type algorithms for determination of hydro-acoustic signatures of surface ships and of divers

Slămnoiu, G; Radu, O; Roşca, Vlad; Pascu, C; Damian, Radu-Florin; Surdu, George; Curcă, E; Radulescu, Adrian

doi:10.1088/1757-899x/145/8/082013

Cited by 6 publications

(3 citation statements)

References 3 publications

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“…A recent study compared four DEMON-type algorithms (Slamnoiu et al, 2016) regarding their ability to detect small ships and divers, as well as their robustness to acoustic noise. The same authors (Slamnoiu et al, 2016) introduced a variation of the classic algorithms that was shown to yield similar results with less computational effort, also arguing that their modified DEMON algorithm is more robust to noise. Chung et al (2011) described an approach for identification and classification of multiple ships in busy harbour conditions using the DEMON algorithm.…”

Section: Rel Ated Workmentioning

confidence: 99%

“…While some solutions have employed classification approaches derived from the analysis of acoustic features (Leal, Leal, & Sanchez, 2015), others have focused on the specific spectral signatures of the sounds emitted by the different types of vessels, for example training neural networks to detect a set of known signatures (Chung, Sutin, Sedunov, & Bruno, 2011;Hanson, Antoni, Brown, & Emslie, 2008;Pollara, Lignan, Boulange, Sutin, & Salloum, 2017;Slamnoiu et al, 2016). Nonetheless, improving detection accuracy in noisy conditions and reducing the rate of false positives remain as challenges.…”

Section: Introductionmentioning

confidence: 99%

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Automatic detection of vessel signatures in audio recordings with spectral amplitude variation signature

2019

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Sound emissions by ships and boats can strongly impact marine life, with potential to affect communications, breeding and prey and predator relationships. Automatic detection of boat signatures in underwater audio recordings is thus an important task. Automated solutions are particularly relevant for monitoring preservation areas where the presence of watercrafts is usually regulated. The task is particularly challenging because it requires distinguishing multiple overlapping acoustic events in typically noisy audio recordings. In this paper, we introduce an algorithm for boat and ship detection which computes an acoustic signature that captures the variance in the frequency amplitudes observed over the duration of the signal. We evaluated the algorithm on a database of underwater recordings collected at two conservation areas in the State of São Paulo, Brazil, with very good results, and also compared it with an existing solution. Besides being effective, the algorithm requires limited user input and no parameter fine tuning to handle diverse situations. It thus provides a solution to automate the detection of vessels, with potential applications for monitoring marine preservation areas.

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Section: Rel Ated Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic detection of vessel signatures in audio recordings with spectral amplitude variation signature

2019

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“…Even though these signals are often considered to be noise for telecommunication systems due to their negative impacts on transmission, they are extremely useful for passive sonar, because they carry the full characteristics of the target. During movement, the main noise source of each ship is the cavitation of the propeller blades (accounted for about 80–85% of the noise intensity generated in the marine environment) [ 7 ]. The characteristics of this noise depend on the rotation frequency of the propeller blades.…”

Section: Introductionmentioning

confidence: 99%

Classification of Surface Vehicle Propeller Cavitation Noise Using Spectrogram Processing in Combination with Convolution Neural Network

Bach¹,

Vu²,

Nguyen

2021

Sensors

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This paper proposes a method to enhance the quality of detecting and classifying surface vehicle propeller cavitation noise (VPCN) in shallow water by using the improved Detection Envelope Modulation On Noise (DEMON) algorithm in combination with the modified Convolution Neural Network (CNN). To improve the quality of the VPCN spectrogram signal, we apply the DEMON algorithm while analyzing the amplitude variation (AV) to detect the fundamental frequencies of the VPCN signal. To enhance the performance of the traditional CNN, we adapt the size of the sliding window in accordance with the properties of the VPCN spectrogram data, and also reconstruct the CNN layer structure. As for the results, the fundamental frequencies contented in the VPCN spectrogram data can be detected. The analytical results based on the measured data show that the accuracy of the VPCN classification obtained by the proposed method is above 90%, which is higher than those obtained by traditional methods.

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Novel DEMON Spectra Analysis Techniques and Empirical Knowledge Based Reference Criterion for Acoustic Signal Classification

Hashmi

Raza

2022

J. Electr. Eng. Technol.

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This paper presents some novel methods to estimate a vessel’s number of shafts, course, speed and classify it using the underwater acoustic noise it generates. A classification framework as well as a set of reference parameters for comparison are put forth. Identifying marine traffic in surroundings is an important task for vessels in an open sea. Vessels in vicinity can be identified using their signatures. One of the typical signatures emitted by a vessel is its acoustic measurements. The raw sonar data consisting of the acoustic signatures is generally observed manually by sonar operators for suggesting class of query vessel. The valuable information that can be extracted from the recorded acoustic signature includes shaft revolutions per minute (SRPM), number of blades (NOB), number of shafts, course and speed etc. Expert sonar operators use their empirical knowledge to estimate a vessel’s SRPM and NOB. Based on this information vessel classification is performed. Empirical knowledge comes with experience, and the manual process is prone to human error. To make the process systematic, calculation of the parameters of the received acoustic samples can be visually analyzed using Detection of Envelope Modulation on Noise (DEMON) spectra. Reported research mostly focuses on SRPM and NOB. Parameters such as number of shafts and vessel course and speed can effectively aid the vessel classification process. This paper makes three novel contributions in this area. Firstly, some novel DEMON spectra analysis techniques are proposed to estimate a water vessel’s number of shafts, speed, and relative course. Secondly, this paper presents a classification framework that uses the features extracted from DEMON spectra and compares them with a reference set. Thirdly, a novel set of reference parameters are provided that aid classification into categories of large merchant ship type 1, large merchant ship type 2, large merchant ship type 3, medium merchant ship, oiler, car carrier, cruise ship, fishing boat and fishing trawler. The proposed analysis and classification techniques were assessed through trials with 877 real acoustic signatures recorded under varying conditions of ship’s speed and sea state. The classification trials revealed a high accuracy of 94.7%.

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DEMON-type algorithms for determination of hydro-acoustic signatures of surface ships and of divers

Cited by 6 publications

References 3 publications

Automatic detection of vessel signatures in audio recordings with spectral amplitude variation signature

Automatic detection of vessel signatures in audio recordings with spectral amplitude variation signature

Classification of Surface Vehicle Propeller Cavitation Noise Using Spectrogram Processing in Combination with Convolution Neural Network

Novel DEMON Spectra Analysis Techniques and Empirical Knowledge Based Reference Criterion for Acoustic Signal Classification

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