Underwater sound classification based on Gammatone filter bank and Hilbert-Huang transform

Zeng, Xiangyang; Wang, Shuguang

doi:10.1109/icspcc.2014.6986287

Cited by 13 publications

(3 citation statements)

References 19 publications

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“…Feature extraction methods, such as the short-time Fourier transform (STFT) (Gabor, 1946), the discrete wavelet transform (DWT) (Mallat, 1989), the Hilbert-Huang transform (Yu et al, 2016), and the limit cycle (Goldobin et al, 2010), have been proven to be simple yet effective in acoustic signal processing (Zeng and Wang, 2014;Liu et al, 2017;Tuncer et al, 2021). These methods mainly focus on time domain features and have succeeded due to the assumption of a homogenous propagation environment, such as air, where the frequency characteristics of received signals remain constant over time (Salomons and Havinga, 2015).…”

Section: Introductionmentioning

confidence: 99%

Fast ship radiated noise recognition using three-dimensional mel-spectrograms with an additive attention based transformer

Wang,

Zhang,

Huang

2023

Front. Mar. Sci.

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Passive recognition of ship-radiated noise plays a crucial role in military and economic domains. However, underwater environments pose significant challenges due to inherent noise, reverberation, and time-varying acoustic channels. This paper introduces a novel approach for ship target recognition and classification by leveraging the power of three-dimensional (3D) Mel-spectrograms and an additive attention based Transformer (ADDTr). The proposed method utilizes 3D Mel-spectrograms to capture the temporal variations in both target signal and ambient noise, thereby enhancing both categories’ distinguishable characteristics. By incorporating an additional spatial dimension, the modeling of reverberation effects becomes possible. Through analysis of spatial patterns and changes within the spectrograms, distortions caused by reverberation can be estimated and compensated, so that the clarity of the target signals can be improved. The proposed ADDTr leverages an additive attention mechanism to focus on informative acoustic features while suppressing the influence of noisy or distorted components. This attention-based approach not only enhances the discriminative power of the model but also accelerates the recognition process. It efficiently captures both temporal and spatial dependencies, enabling accurate analysis of complex acoustic signals and precise predictions. Comprehensive comparisons with state-of-the-art acoustic target recognition models on the ShipsEar dataset demonstrate the superiority of the proposed ADDTr approach. Achieving an accuracy of 96.82% with the lowest computation costs, ADDTr outperforms other models.

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Section: Introductionmentioning

confidence: 99%

Fast ship radiated noise recognition using three-dimensional mel-spectrograms with an additive attention based transformer

Wang,

Zhang,

Huang

2023

Front. Mar. Sci.

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“…Many machine learning methods increase the recognition rate while also increasing the complexity of models [6,7]. This will undoubtedly affect the recognition efficiency, and the recognition rate will also be affected by the underwater environment.…”

Section: Introductionmentioning

confidence: 99%

Research on a random forest-based underwater target image recognition method

Chen¹,

Song²,

Wei-bo³

et al. 2017

International Symposium on Computer Science and Artificial Intelligence(ISCSAI)

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Aimed to solve the problem of the low recognition rate of underwater images in bad underwater environment, an underwater target images' gray level pixel matrix has been employed as the feature matrices to train random forest classifier and get the final classification result from the votes of multi decision trees in this paper. The Gaussian noise with different variances are added into the underwater acoustic image to simulate the different levels of complexity in underwater environment, by which the recognition rate and false alarm rate of diver, fish, propeller, bubble and pipeline are obtained. Furthermore, the performance of this method by ROC curve and AUC are analyzed, and the simulation results show that if the SNR (Signal-to-noise ratio) of underwater target is not less than 2.62dB or the PSNR (Peak signal-to-noise ratio) is not less than 12.7dB, the method proposed in this paper can detect underwater target with weak signal, and achieve more than 90% of the target recognition rate.

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“…Potential applications for GAFs include: multiplexers [13], designing rainbow sensors, [14], analyzing seismic signals [15], underwater sound classification [16], cochlear implants [17], and hearing aids [18]. Due to the nature of these applications, any discussion of GAFs must include a discussion of filterbank representations.…”

Section: B Fractional-exponent Generalized Auditory Filters and Filte...mentioning

confidence: 99%

Design of FIR filters with magnitude specifications

Alkhairy¹

Conference Record of the Thirtieth Asilomar Conference on Signals, Systems and Computers

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In this paper, we present a method for the design of an FIR filter that minimizes the Chebyshev error in the magnitude frequency domain. The method exhibits desirable properties similar to those characterizing the Parks-McClellan algorithm and requires less than seven iterations to solve the nonlinear filter design problem. The O ( N 2 ) iterative method yields filters that are 40% better than those designed using conventional techniques and results in filters that are 30% shorter.

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Underwater sound classification based on Gammatone filter bank and Hilbert-Huang transform

Cited by 13 publications

References 19 publications

Fast ship radiated noise recognition using three-dimensional mel-spectrograms with an additive attention based transformer

Fast ship radiated noise recognition using three-dimensional mel-spectrograms with an additive attention based transformer

Research on a random forest-based underwater target image recognition method

Design of FIR filters with magnitude specifications

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