A new way for underwater acoustic signal analysis: The morphological filtering

Moreaud, Ugo; Courmontagne, Philippe; Chaillan, Fabien; Mesquida, Jean-Remi; Ouelha, Samir

doi:10.1109/oceans-genova.2015.7271406

Cited by 4 publications

(3 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Take the Db obtained through intra-RNN and use it as input to inter-RNN, where the RNN is applied to the last dimension, as shown in Equation (7).…”

Section: Dprnn Structurementioning

confidence: 99%

“…At this stage, traditional denoising methods [2,3] usually use transformation analysis methods and time-tofrequency conversion methods to achieve signal enhancement. For example, amplitude-aware permutation entropy [4] wavelet and block thresholding [5,6], multi-directional filters [7], minimum variance distortion less response [8], variational approach [9], wavelet transform [10], empirical mode decomposition [11][12][13], linear spectrum [14], singular value decomposition [15].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A novel noise reduction technique for underwater acoustic signals based on dual‐path recurrent neural network

2022

View full text Add to dashboard Cite

A dual‐path recurrent neural network model is proposed to achieve noise reduction of underwater acoustic signals, which consists of three steps: feature extraction, mask separation, and signal recovery. For feature extraction, we use a multi‐scale convolutional neural network to extract higher‐order non‐linear features of the input signal and chunk the obtained non‐linear features into subvectors with fixed lengths according to the temporal dimension. In the mask separation, two recurrent neural networks based on constructing a dual‐path network, a bidirectional network for extracting intra‐features, and a directional network for extracting inter‐features is build. Finally, overlapping and permutation neural networks are used to recover the denoised acoustic signal. By comparing different denoising methods, it can be seen that this method is effective in underwater acoustic signals. By evaluating the two tasks of the ShipsEar dataset, this model can improve the signal‐to‐noise ratio by 12.02 and 9.48 dB, respectively.

show abstract

“…Take the Db obtained through intra-RNN and use it as input to inter-RNN, where the RNN is applied to the last dimension, as shown in Equation (7).…”

Section: Dprnn Structurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A novel noise reduction technique for underwater acoustic signals based on dual‐path recurrent neural network

2022

View full text Add to dashboard Cite

show abstract

“…At this stage, conventional denoising methods usually use transformation analysis methods and time-to-frequency conversion methods to achieve signal enhancement [2] [3]. For example, Amplitude-Aware Permutation Entropy [4] Wavelet and Block Thresholding [5] [6], multi-directional filters [7], Minimum Variance Distortionless Response [8], Variational Approach [9], Wavelet Transform [10], Empirical Mode Decomposition [11] [12] [13], Linear Spectrum [14], Singular Value Decomposition [15].…”

Section: Introductionmentioning

confidence: 99%

Method of Underwater Acoustic Signal Denoising Based on Dual-Path Transformer Network

2024

View full text Add to dashboard Cite

The presence of natural ambient noise interferes with the system for locating and identifying underwater targets. This paper suggests that a Dual-Path Transformation Network(DPTN) reduces ambient noise in underwater acoustic signals. First, the input acoustic signals' higher-order non-linear features are extracted using a multi-scale convolutional encoder neural network. Second, sub-vectors with the same length are created according to the time dimension from the higher-order non-linear features. The subvectors are stitched together to form a three-dimensional tensor. Third, a neural network transformer based on the feed-forward network is constructed. Further, to capture long-term series features and separate the target signal from the noisy signals, the three-dimensional tensor is used as the input of the transformerbased masking network. Finally, overlap-add and transpose are used to obtain discernible target signals. The experimental results verify the effectiveness of the proposed underwater acoustic signal denoising algorithm and demonstrate that the proposed DPRN method can obtain higher output signal-to-noise ratio (SNR) and the scale-invariant signal-to-noise ratio (SI-SNR) compared with the other classical algorithms.

show abstract

Performance assessment of noise reduction methods applied to underwater acoustic signals

Moreaud

Courmontagne

Chaillan³

et al. 2016

OCEANS 2016 MTS/IEEE Monterey

View full text Add to dashboard Cite

International audienceThis paper presents an objective evaluation of state-of-the-art single channel noise reduction algorithms. 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. Moreover, this first quantitative evaluation is followed by a perceptual evaluation based on the scores obtained from peoples ranking the quality of each record, with a simple rating rule running from one (poor) to five (perfect). Finally, the results obtained with the denoising methods selected in this study are described to establish the pros and cons of each method, when compared, according to our proposed evaluation criteria

show abstract

A new way for underwater acoustic signal analysis: The morphological filtering

Cited by 4 publications

References 5 publications

A novel noise reduction technique for underwater acoustic signals based on dual‐path recurrent neural network

A novel noise reduction technique for underwater acoustic signals based on dual‐path recurrent neural network

Method of Underwater Acoustic Signal Denoising Based on Dual-Path Transformer Network

Performance assessment of noise reduction methods applied to underwater acoustic signals

Contact Info

Product

Resources

About