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Studying Cavitation Enhanced Therapy

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Reducing ambient noise diffusion model for underwater acoustic target.

Yunqi Zhang¹, Jiansen Hao¹, Qunfeng Zeng¹

¹Department of Mechanical Engineering, Xi'an Jiaotong University, Shanxi 710049, China.

Neural Networks : the Official Journal of the International Neural Network Society

|April 13, 2025

Summary

This summary is machine-generated.

This study introduces a novel diffusion model method to reduce underwater ambient noise, improving acoustic target recognition. The proposed Reducing Ambient Noise Diffusion (RAND) model effectively cleans audio signals for better performance.

Keywords:

Decapitation normalization Diffusion model Noise reduction Underwater acoustics

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Area of Science:

Acoustics
Signal Processing
Machine Learning

Background:

Underwater acoustic target recognition is hindered by irregular ambient noise.
Diffusion models show promise in audio processing, particularly for human voice, suggesting potential for underwater acoustics.

Purpose of the Study:

To propose a general method for reducing ambient noise in underwater acoustic signals using diffusion models.
To enhance the effectiveness of underwater acoustic target recognition by mitigating noise interference.

Main Methods:

A Decapitation normalization method was developed to balance data distribution across frequency scales and unify noise addition.
A Reducing Ambient Noise Diffusion (RAND) Model was proposed, leveraging diffusion principles for noise removal.
A Three-condition mask method was introduced to improve model robustness during the sampling process.

Main Results:

The proposed Decapitation normalization method effectively balances data distribution and unifies noise addition.
The RAND Model demonstrated effective ambient noise removal within a limited number of steps.
The Three-condition mask method enhanced the model's robustness during sampling.

Conclusions:

The developed diffusion model-based method significantly reduces ambient noise in underwater acoustic signals.
The proposed techniques, including Decapitation normalization and the RAND Model, offer a viable solution for improving underwater acoustic target recognition.