Classification of underwater transient signals using MFCC feature vector

Lim, Tae-Gyun; Bae, Kyung‐Yeol; Hwang, Chan-Sik; Lee, Hyeonguk

doi:10.1109/isspa.2007.4555521

Cited by 12 publications

(5 citation statements)

References 7 publications

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“…Yang et al [14] Since the 1990s, researchers have been integrating signal analysis theory with machine learning techniques, utilizing handcrafted feature identifiers to extract attributes from underwater acoustic signals. These attributes include the Zero-crossing Rate (ZCR), Wavelet Transform (WT) [8], Hilbert-Huang Transform (HHT), Higher-order Spectral Estimation, and Mel-frequency Cepstral Coefficient (MFCC) [9]. Machine learning classifiers such as Bayesian, Decision Tree, and Support Vector Machines (SVMs) [10] are then employed to identify and classify underwater targets.…”

Section: Methods Based On Traditional Machine Learningmentioning

confidence: 99%

Mobile_ViT: Underwater Acoustic Target Recognition Method Based on Local–Global Feature Fusion

Yao,

Gao,

Wang

et al. 2024

JMSE

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To overcome the challenges of inadequate representation and ineffective information exchange stemming from feature homogenization in underwater acoustic target recognition, we introduce a hybrid network named Mobile_ViT, which synergizes MobileNet and Transformer architectures. The network begins with a convolutional backbone incorporating an embedded coordinate attention mechanism to enhance the local details of inputs. This mechanism captures the long-term temporal dependencies and precise frequency–domain relationships of signals, focusing the features on the time–frequency positions. Subsequently, the Transformer’s Encoder is integrated at the end of the backbone to facilitate global characterization, thus effectively overcoming the convolutional neural network’s shortcomings in capturing long-range feature dependencies. Evaluation on the Shipsear and DeepShip datasets yields accuracies of 98.50% and 94.57%, respectively, marking a substantial improvement over the baseline. Notably, the proposed method also demonstrates obvious separation coefficients, signifying enhanced clustering effectiveness, and is lighter than other Transformers.

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Section: Methods Based On Traditional Machine Learningmentioning

confidence: 99%

Mobile_ViT: Underwater Acoustic Target Recognition Method Based on Local–Global Feature Fusion

Yao,

Gao,

Wang

et al. 2024

JMSE

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“…Perform DCT on the M logarithm energies calculated by Formula (4) to obtain the MFCC of order L (L = 12-16), where the DCT is [20]: In practical application, the cepstrum difference parameter (delta cepstrum) is calculated following the value of L MFCC cepstrum coefficients, which is expressed as [18]:…”

Section: Mfccmentioning

confidence: 99%

Seal call recognition based on general regression neural network using Mel-frequency cepstrum coefficient features

Yao

Wang

Yang

et al. 2023

EURASIP J. Adv. Signal Process.

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In this paper, general regression neural network (GRNN) with the input feature of Mel-frequency cepstrum coefficient (MFCC) is employed to automatically recognize the calls of leopard, ross, and weddell seals with widely overlapping living areas. As a feedforward network, GRNN has only one network parameter, i.e., spread factor. The recognition performance can be greatly improved by determining the spread factor based on the cross-validation method. This paper selects the audio data of the calls of the above three kinds of seals and compares the recognition performance of three machine learning models for inputting MFCC features and low-frequency analyzer and recorder (LOFAR) spectrum. The results show that at the same signal-to-noise ratio (SNR), the recognition result of the MFCC feature is better than that of the LOFAR spectrum, which is verified by statistical histogram. Compared with other models, GRNN for inputting MFCC features has better recognition performance and can still achieve effective recognition at low SNRs. Specifically, the accuracy is 97.36%, 93.44%, 92.00% and 88.38% for cases with an infinite SNR and SNR of 10, 5 and 0 dB, respectively. In particular, GRNN has the least training and testing time. Therefore, all results show that the proposed method has excellent performance for the seal call recognition.

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“…Traditional algorithm, such as GMM [ 13 ] and SVM [ 14 ], were used for the underwater acoustic field. These manually extracted features and algorithms have played a significant role in the underwater acoustic field, for example, MFCC features were extracted in a lot of work [ 15 , 16 , 17 ] for UATR. Xin et al note in the classification of ship-radiated noise signals that the traditional classifier has many limitations.…”

Section: Introductionmentioning

confidence: 99%

A Novel Underwater Acoustic Target Recognition Method Based on MFCC and RACNN

Liu,

Yang,

Hou

et al. 2024

Sensors

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In ocean remote sensing missions, recognizing an underwater acoustic target is a crucial technology for conducting marine biological surveys, ocean explorations, and other scientific activities that take place in water. The complex acoustic propagation characteristics present significant challenges for the recognition of underwater acoustic targets (UATR). Methods such as extracting the DEMON spectrum of a signal and inputting it into an artificial neural network for recognition, and fusing the multidimensional features of a signal for recognition, have been proposed. However, there is still room for improvement in terms of noise immunity, improved computational performance, and reduced reliance on specialized knowledge. In this article, we propose the Residual Attentional Convolutional Neural Network (RACNN), a convolutional neural network that quickly and accurately recognize the type of ship-radiated noise. This network is capable of extracting internal features of Mel Frequency Cepstral Coefficients (MFCC) of the underwater ship-radiated noise. Experimental results demonstrate that the proposed model achieves an overall accuracy of 99.34% on the ShipsEar dataset, surpassing conventional recognition methods and other deep learning models.

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Classification of underwater transient signals using MFCC feature vector

Cited by 12 publications

References 7 publications

Mobile_ViT: Underwater Acoustic Target Recognition Method Based on Local–Global Feature Fusion

Mobile_ViT: Underwater Acoustic Target Recognition Method Based on Local–Global Feature Fusion

Seal call recognition based on general regression neural network using Mel-frequency cepstrum coefficient features

A Novel Underwater Acoustic Target Recognition Method Based on MFCC and RACNN

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