Underwater noises: Statistical modeling, detection, and normalization

Bouvet, Michel; Schwartz, S.C.

doi:10.1121/1.396047

Cited by 42 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To provide a consistent and controlled investigation of the robustness of object detection algorithms under varying signalto-noise ratio (SNR) conditions, we introduced an artificial deterioration of the SNR using Normally-distributed random noise. This method is commonly employed in simulations to approximate the random noise typically found in real-world environments and is very similar to models that aim to model the spectrum of background noise underwater [35]. Further, as can been seen in Figure 4, this distribution also fits the TS distribution observed in our experiment.…”

Section: B Detection Accuracy In the Presence Of Noisesupporting

confidence: 69%

Fish Monitoring in Aquaculture Using Multibeam Echosounders and Machine Learning

Kristmundsson,

Patursson,

Potter

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Offshore salmon aquaculture is a growing industry that faces challenges such as sea lice infestations and varying environmental conditions, necessitating the development of new monitoring systems to improve fish welfare and sustainability. In this paper, we propose and test a machine learning based method for underwater detection and localisation using multibeam echosounders (MBES) in fish farming applications. We demonstrate a three-stage process involving data acquisition, pre-processing, and object detection. We then compare the performance of four different vision based deep learning object detection algorithms in different signal-to-noise scenarios by artificially adding noise to the pre-beamformed signals. This method successfully detects fish in MBES images, which has potential applications in optimising feeding schedules, behaviour analysis, and fish health monitoring. Furthermore, this method holds potential for the detection and tracking of other objects within fish farms, such as cages and mooring lines. This study paves the way for further development of MBES data being used as a non-invasive and automated monitoring method in aquaculture.

show abstract

Section: B Detection Accuracy In the Presence Of Noisesupporting

confidence: 69%

Fish Monitoring in Aquaculture Using Multibeam Echosounders and Machine Learning

Kristmundsson,

Patursson,

Potter

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The results highlight the dominating frequency components in radiated noise as well as its contributing sources like engine and auxiliaries. Bouvet and Schwartz [12], from the statistical study conducted on underwater noises, state that the underwater background noise characteristics are very similar to Gaussian and the vessel noise can be effectively described by a Gaussian mixture model. Supriya et al [13] propose a method based on spectral subtraction for alleviating the acoustic ambient noise with underwater acoustic receivers.…”

Section: Related Workmentioning

confidence: 99%

“…The proposed HMM classifier system incorporates a prototype communication system wholly for the purpose of estimating the Rayleigh fading, thereby compensating the ill effects induced in the signals by underwater channel [4]. The Rayleigh fading model represented by ( 7) is modeled using an AR method as in ( 8), ( 9), ( 10), (11), and (12) and is assumed to provide a fading effect closer to the actual one. This Rayleigh fading model is integrated in the prototype communication system for compensating the channel fading effects.…”

Section: Gammatone Cepstralmentioning

confidence: 99%

A GTCC-Based Underwater HMM Target Classifier with Fading Channel Compensation

2018

View full text Add to dashboard Cite

Underwater acoustic target classifiers are found to have many applications in military and security areas where a higher degree of prediction accuracy is needed that makes classifier efficiency and reliability an interesting subject. Classifiers are often trained with known acoustic target specimens with their characteristic feature set and tested with measurements obtained from the sonar that is deployed in the surveillance or observation zone. The selection of source-specific deterministic features in automatic target recognition (ATR) system is very significant, since it determines the reliability, efficiency, and success rate of the classifier. The robustness of the gammatone cepstral coefficients (GTCC) in combination with the statistical Euclidean distance, artificial neural network (ANN), and hidden Markov model (HMM) classifiers has been investigated, and its performance is compared with that of other feature extraction schemes. The classifier performance has been analyzed in Rayleigh fading conditions, based on which the performance is enhanced by incorporating an autoregressive (AR) Rayleigh fading channel compensation. The performance of the classifier in different operating conditions is investigated, with underwater target signals consisting of the real field data collected during expedition, and the results are presented in this paper.

show abstract

“…In the literature, various noise models have been introduced to model noise with non-Gaussian characteristics [6][7][8][9][10][11][12][13][14][15][16]. Back in 1988, M. Bouvet [8] used Gaussian-Gaussian mixture (GGM) and Middleton Class-A models to describe underwater noise data. The two models showed better results than Gaussian model, but GGM was suitable for noise which is close to Gaussian and Middleton Class-A model was too strict to use.…”

Section: Introductionmentioning

confidence: 99%

The Alpha stable distribution in ocean ambient noise modelling

Song

Guo

2019

MATEC Web Conf.

View full text Add to dashboard Cite

In view of the non-Gaussian of ocean ambient noise, the  stable distribution is applied to the statistical modelling. Firstly, the one-to-one correspondence between the four parameters of stable distribution and the sample mean, variance, skewness and kurtosis are established according to physical meaning. Then, numerical simulations are conducted to analyze the suitability of stable distribution for non-Gaussian ambient noise. In the case of white noise interference, noise is divided into Gaussian state, leptokurtic, and platykurtic separately. The parameters of stable distribution are estimated by the sample quantile and characteristic function method jointly. The simulation results show that, in the Gaussian state,  stable distribution is equivalent to normal distribution. As for leptokurtic distribution, stable distribution is much better than normal distribution, indicating absolute predominance in impulse-like data modeling. But it is not adaptive for low kurtosis state because its characteristic exponent can’t be bigger than two. Finally, the result is verified by ambient noise collected in three environmental conditions, such as quiet ambient noise, airgun interference noise and ship noise. In all three cases,  stable distribution shows good adaptability and accuracy, especially for the airgun dataset it is far superior to normal distribution.

show abstract

Underwater noises: Statistical modeling, detection, and normalization

Cited by 42 publications

References 0 publications

Fish Monitoring in Aquaculture Using Multibeam Echosounders and Machine Learning

Fish Monitoring in Aquaculture Using Multibeam Echosounders and Machine Learning

A GTCC-Based Underwater HMM Target Classifier with Fading Channel Compensation

The Alpha stable distribution in ocean ambient noise modelling

Contact Info

Product

Resources

About