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A survey: Noise reduction algorithm for underwater signal

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Underwater acoustics is the study of the propagation of sound in water and the interaction of the mechanical waves that constitute sound with the water and its boundaries. The water may be in the ocean, a lake or a tank. Typical frequencies associated with underwater acoustics are between 10 Hz and 1 MHz.There are two major steps for underwater signal de-noising. The first step deals with signal pre-processing which including amplifying, filtering, and make use of analog/digital (AD) technique to save signals as a digital file. The underwater acoustic signal is affected by ocean interference and ambient noise disturbance during its propagation in the ocean. Acoustic waves are the most important characteristic to convey data in the underwater domain as a practical method. But the oceans are increasingly exposed to sounds from human activities, such as shipping and the building of foundations for offshore construction projects and other different noise

Because of the activities of people in the ocean are expanded, the field of underwater acoustics has been extensively developed in a variety of applications including acoustic communication, the detection and location of surface and subsurface objects, depth sounders, and sub-bottom profiling for seismic exploration[l]. underwater acoustic signals that received from the ocean are the signal of ships radiated when it sails on the ocean. The aim of this paper is to develop a de-noising system and evaluate the effect of wavelet de-noising processing for underwater acoustic signals. Noise hampers sonar data collection and related processing of the data to extract information since many of the signals of interest are of short duration and of relatively low energy. Underwater signal transmission is a challenging task since the usable frequency range is limited to low frequency and the transmission of electromagnetic waves is impossible due to its high attenuation nature.

The types of attenuation that affect the sound signal are transmission loss, Spreading Loss, Attenuation Loss, Background noise like Self-Noise, Machinery Noise, Flow Noise, etc.

1.1 Motivation:

Human interaction is the study of the underwater acoustic signal, which is the rapidly growing topic everywhere; Communication purpose; Commercial; Warship.

Acoustic communications form an active field of research with significant challenges to overcome, especially in horizontal, shallow-water channels.

1.2 Objective

Underwater acoustic telemetry exists in applications such as data harvesting for environmental monitoring, communication with and between manned and unmanned underwater vehicles, transmission of diver speech, etc. Reduce noise in underwater for the acoustic signal. • Sound propagation losses • Self-noise and ambient noise, SNR

A noise removal algorithm based on short-time Wiener filtering is described. An analysis of the performance of the filter in terms of processing gain, mean square error, and signal distortion is presented. Noise hampers sonar data collection and related processing of the data to extract information since many of the signals of interest are of short duration and of relatively low energy [1].

The evaluation is performed on a representative real data set of underwater acoustic records. Rationales used to process the proposed evaluation are mean squared error, global signal-to-noise ratio (SNR), segmental SNR and mean squared spectral error. These filters are generally designed by a calculation which involves the signal autocorrelation estimation, a difficult task in case of low SNR or presence of non-stationary components. Musical noise is a perceptual phenomenon that occurs when isolated peaks remain in the time-frequency representation after processing with spectral subtraction algorithm [2].

The authors S.S.Murugan, et al [3] studied the real-time data collected from the Bay of Bengal at Chennai by implementing Welch, Barlett and Blackman estimation methods and improved the maximum Signal to Noise Ratio to 42-51 dB.

The authors Yen-Hsiang Chen et al [4] implemented a real-time adaptive Wiener filter with two microphones is implemented to reduce noisy speech when noise signals and desired speech are incoming simultaneously. Sound travels rapidly through water – four times faster than the air. As in open air, sounds are transmitted in water as a pressure wave. They can be loud or soft, high- or low pitched, constant or intermittent, and volume decreases with increasing distance from the source. Sound pressure is most commonly measured in decibels (dB).

Underwater noise has been divided into two main types:

• Impulsive: Loud, intermittent or infrequent noises, such as those generated by piling and seismic surveys

• Continuous: Lower-level constant noises, such as those generated by shipping and wind turbines These two types of MSFD-related noise have different impacts on marine life. In addition, mid-frequency naval sonar may be harmful to marine mammals. The frequency or pitch of the noise is also important, as animals are sensitive to different frequencies [5].

The underwater acoustic signal is affected by ocean interference and ambient noise disturbance during its propagation in the ocean. Therefore the signal reveals the random process and time-varying characteristics. The procedure consists of three parts: First, wavelet transformation of the underwater acoustic signals. Secondly, the threshold of wavelet coefficients. Thirdly, inverse wavelet transform of reconstructing modified signals [6]

Because of the activities of people in the ocean are expanded, the field of underwater acoustics has been extensively developed in a variety of applications including acoustic communication, the detection and location of surface and subsurface objects, depth sounders, and sub-bottom profiling for seismic exploration[7].

The Ultrasonic signal is most commonly used for the depth estimation. This signal is affected by various underwater noises which results in inaccurate depth estimation. The objective of this paper is to provide noise reduction methods for the underwater acoustic signal. In present work, the signal processing is done on the data collected using TC2122 dual frequency transducer along with the Navy sound 415 echo sounder. There are two signal processing techniques which are used: The first method is denoising algorithm based on Stationary wavelet transform (SWT) and the second method is Savitzky-Golay filter. The results are evaluated based on the criteria of peak signal to noise ratio and 3D Surfer plots of the dam reservoir whose depth estimation has to be done [8].

Minimize or remove the background noise signals from the corrupted acoustic signal in underwater communication. The background noise signal is given to the adder/ subtractor unit and using Weiner filter a similar noise signal is generated by adjusting the filter coefficients. We are studied about filters. From these filter technique, we can remove noise in the underwater acoustic signal. These are some most useful techniques for noise reduction. I referred so many papers related to this topic. An algorithm for noise removal based on optimal filtering of short segments of the data has been developed. The algorithm was developed for improved processing of underwater acoustic data. Hence the analysis of Ultrasonic signal is done using two techniques: Harr wavelet Transform and Savitzky-Golay filter. It is found that out of all wavelet transforms, harrwavelet is most suitable for noise reduction in ultrasonic signal because PSNR value is high among all the wavelets used.

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