Underwater Acoustic Signal Denoising Algorithms: A Survey of the State-of-the-art

• URL: http://arxiv.org/abs/2407.13264v1
• Date: Thu, 18 Jul 2024 08:14:59 GMT
• Title: Underwater Acoustic Signal Denoising Algorithms: A Survey of the State-of-the-art
• Authors: Ruobin Gao, Maohan Liang, Heng Dong, Xuewen Luo, P. N. Suganthan,
• Abstract summary: Despite significant progress in the field, the complex nature of underwater environments poses unique challenges that complicate the denoising process. We begin by outlining the fundamental challenges associated with underwater acoustic signal processing, including signal attenuation, noise variability, and the impact of environmental factors. The review then systematically categorizes and discusses various denoising algorithms, such as conventional, decomposition-based, and learning-based techniques, highlighting their applications, advantages, and limitations.
• Score: 8.874900087462134
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper comprehensively reviews recent advances in underwater acoustic signal denoising, an area critical for improving the reliability and clarity of underwater communication and monitoring systems. Despite significant progress in the field, the complex nature of underwater environments poses unique challenges that complicate the denoising process. We begin by outlining the fundamental challenges associated with underwater acoustic signal processing, including signal attenuation, noise variability, and the impact of environmental factors. The review then systematically categorizes and discusses various denoising algorithms, such as conventional, decomposition-based, and learning-based techniques, highlighting their applications, advantages, and limitations. Evaluation metrics and experimental datasets are also reviewed. The paper concludes with a list of open questions and recommendations for future research directions, emphasizing the need for developing more robust denoising techniques that can adapt to the dynamic underwater acoustic environment.

Related papers

• Towards Robust Transcription: Exploring Noise Injection Strategies for Training Data Augmentation [55.752737615873464]
  This study investigates the impact of white noise at various Signal-to-Noise Ratio (SNR) levels on state-of-the-art APT models. We hope this research provides valuable insights as preliminary work toward developing transcription models that maintain consistent performance across a range of acoustic conditions.
  arXiv  Detail & Related papers  (2024-10-18T02:31:36Z)
• A Self-Supervised Denoising Strategy for Underwater Acoustic Camera Imageries [3.0918473503782042]
  This paper introduces a novel strategy for denoising acoustic camera images using deep learning techniques. It successfully removes noise while preserving fine feature details, thereby enhancing the performance of local feature matching.
  arXiv  Detail & Related papers  (2024-06-05T04:07:37Z)
• Quantifying Noise of Dynamic Vision Sensor [49.665407116447454]
  Dynamic visual sensors (DVS) are characterised by a large amount of background activity (BA) noise. It is difficult to distinguish between noise and the cleaned sensor signals using standard image processing techniques. A new technique is presented to characterise BA noise derived from the Detrended Fluctuation Analysis (DFA)
  arXiv  Detail & Related papers  (2024-04-02T13:43:08Z)
• Underwater Acoustic Signal Recognition Based on Salient Feature [9.110359213246825]
  This paper proposes a method utilizing neural networks for underwater acoustic signal recognition. The proposed approach involves continual learning of features extracted from spectra for the classification of underwater acoustic signals.
  arXiv  Detail & Related papers  (2023-12-20T16:04:02Z)
• Explainable Artificial Intelligence driven mask design for self-supervised seismic denoising [0.0]
  Self-supervised coherent noise suppression methods require extensive knowledge of the noise statistics. We propose the use of explainable artificial intelligence approaches to see inside the black box that is the denoising network. We show that a simple averaging of the Jacobian contributions over a number of randomly selected input pixels, provides an indication of the most effective mask.
  arXiv  Detail & Related papers  (2023-07-13T11:02:55Z)
• Learning-based estimation of in-situ wind speed from underwater acoustics [58.293528982012255]
  We introduce a deep learning approach for the retrieval of wind speed time series from underwater acoustics. Our approach bridges data assimilation and learning-based frameworks to benefit both from prior physical knowledge and computational efficiency.
  arXiv  Detail & Related papers  (2022-08-18T15:27:40Z)
• Removing Noise from Extracellular Neural Recordings Using Fully Convolutional Denoising Autoencoders [62.997667081978825]
  We propose a Fully Convolutional Denoising Autoencoder, which learns to produce a clean neuronal activity signal from a noisy multichannel input. The experimental results on simulated data show that our proposed method can improve significantly the quality of noise-corrupted neural signals.
  arXiv  Detail & Related papers  (2021-09-18T14:51:24Z)
• Discriminative Singular Spectrum Classifier with Applications on Bioacoustic Signal Recognition [67.4171845020675]
  We present a bioacoustic signal classifier equipped with a discriminative mechanism to extract useful features for analysis and classification efficiently. Unlike current bioacoustic recognition methods, which are task-oriented, the proposed model relies on transforming the input signals into vector subspaces. The validity of the proposed method is verified using three challenging bioacoustic datasets containing anuran, bee, and mosquito species.
  arXiv  Detail & Related papers  (2021-03-18T11:01:21Z)
• Dynamic Layer Customization for Noise Robust Speech Emotion Recognition in Heterogeneous Condition Training [16.807298318504156]
  We show that we can improve performance by dynamically routing samples to specialized feature encoders for each noise condition. We extend these improvements to the multimodal setting by dynamically routing samples to maintain temporal ordering.
  arXiv  Detail & Related papers  (2020-10-21T18:07:32Z)
• Simultaneous Denoising and Dereverberation Using Deep Embedding Features [64.58693911070228]
  We propose a joint training method for simultaneous speech denoising and dereverberation using deep embedding features. At the denoising stage, the DC network is leveraged to extract noise-free deep embedding features. At the dereverberation stage, instead of using the unsupervised K-means clustering algorithm, another neural network is utilized to estimate the anechoic speech.
  arXiv  Detail & Related papers  (2020-04-06T06:34:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.