Underwater Transient Signal Classification Using Binary Pattern Image of MFCC and Neural Network

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

doi:10.1093/ietfec/e91-a.3.772

Cited by 11 publications

(5 citation statements)

References 4 publications

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“…The human auditory system can identify sounds of interest in highly complex environments. Researchers have investigated the human auditory system in depth and developed a series of feature extraction methods based on auditory attributes, mainly including Mel Frequency Cepstral Coefficients (MFCC) [10], Gammtone Filter Cepstral Coefficients (GFCC) [11], Linear Prediction Cepstral Coefficient (LPCC) [12], Perceptual Linear Predictive (PLP) [13], and timbre features and loudness features (Zwicker loudness). This paper performs experiments on the above auditory domain features on a dataset of underwater target radiated noise.…”

Section: 23mentioning

confidence: 99%

Research on Feature Extraction of Underwater Acoustic Target Radiation Noise Based on Machine Learning Algorithm

Liu,

Han,

Zhang

et al. 2023

J. Phys.: Conf. Ser.

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Underwater acoustic target recognition is a very important technology in the field of underwater acoustics, with great economic and military value. Feature extraction technology for underwater acoustic target radiation noise signals is the key to achieving acoustic target recognition. This study aims at the feature extraction task of acoustic targets and extracts 10 types of 252-dimensional feature vectors from three domains: time domain, frequency domain, and auditory domain. Through 7 machine learning algorithms for classification and recognition experiments, the experimental results show that the recognition performance of the ensemble classifier is much better than that of a single classifier. For different types of features, this study combines three ensemble learning algorithms and feature selection algorithms to select the original 252-dimensional features. The feature selection experiment shows that the wrapper feature selection algorithm has the best effect, and the feature vector dimension can be reduced to 40 dimensions. The recognition accuracy rate is not less than 92.8%, which provides feature extraction guidance for acoustic target recognition based on feature extraction.

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Section: 23mentioning

confidence: 99%

Research on Feature Extraction of Underwater Acoustic Target Radiation Noise Based on Machine Learning Algorithm

Liu,

Han,

Zhang

et al. 2023

J. Phys.: Conf. Ser.

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“…따라서 선박, 잠수함, 어뢰 등에서 발생된 수중 소나 신호로부터 유용한 특징 정보를 추출하고 표적을 효율적, 자동적으로 식별하기 위한 연 구가 진행되고 있다 (Khotanzed et al, 1989;Larkin, 1997) (Chen, 1985;Boashash and O'shea, 1990;Hemminger and Pao, 1994;Kicinski, 2003;Jiang et al, 2006;임태 균, 2007). 인 잡음을 적용시켜왔다 (박정현 et al, 2007;Lim et al, 2008).…”

Section: 서 론unclassified

Classification of Transient Signals in Ocean Background Noise Using Bayesian Classifier

Kim¹,

Bok²,

Paeng³

et al. 2012

Journal of Ocean Engineering and Technology

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In this paper, a Bayesian classifier based on PCA (principle component analysis) is proposed to classify underwater transient signals using 16 th order LPC (linear predictive coding) coefficients as feature vector. The proposed classifier is composed of two steps. The mechanical signals were separated from biological signals in the first step, and then each type of the mechanical signal was recognized in the second step. Three biological transient signals and two mechanical signals were used to conduct experiments. The classification ratios for the feature vectors of biological signals and mechanical signals were 94.75% and 97.23%, respectively, when all 16 order LPC vector were used. In order to determine the effect of underwater noise on the classification performance, underwater ambient noise was added to the test signals and the classification ratio according to SNR (signal-to-noise ratio) was compared by changing dimension of feature vector using PCA. The classification ratios of the biological and mechanical signals under ocean ambient noise at 10dB SNR, were 0.51% and 100% respectively. However, the ratios were changed to 53.07% and 83.14% when the dimension of feature vector was converted to three by applying PCA. For correct, classification, it is required SNR over 10 dB for three dimension feature vector and over 30dB SNR for seven dimension feature vector under ocean ambient noise environment.

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“…The Mel filter bank has been widely used in feature extraction. It was designed for imitating the band pass filter bank features of the human ear, and it has been the foundation of most speech processing, such as underwater acoustic target recognition, speaker recognition [11][12][13][14][15]. The cepstral or energy features are obtained from the Mel filter bank, which is known as the Mel frequency cepstral coefficient (MFCC).…”

Section: Introductionmentioning

confidence: 99%

Underwater Acoustic Target Recognition: A Combination of Multi-Dimensional Fusion Features and Modified Deep Neural Network

2019

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A method with a combination of multi-dimensional fusion features and a modified deep neural network (MFF-MDNN) is proposed to recognize underwater acoustic targets in this paper. Specifically, due to the complex and changeable underwater environment, it is difficult to describe underwater acoustic signals with a single feature. The Gammatone frequency cepstral coefficient (GFCC) and modified empirical mode decomposition (MEMD) are developed to extract multi-dimensional features in this paper. Moreover, to ensure the same time dimension, a dimension reduction method is proposed to obtain multi-dimensional fusion features in the original underwater acoustic signals. Then, to reduce redundant features and further improve recognition accuracy, the Gaussian mixture model (GMM) is used to modify the structure of a deep neural network (DNN). Finally, the proposed underwater acoustic target recognition method can obtain an accuracy of 94.3% under a maximum of 800 iterations when the dataset has underwater background noise with weak targets. Compared with other methods, the recognition results demonstrate that the proposed method has higher accuracy and strong adaptability.

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Underwater Transient Signal Classification Using Binary Pattern Image of MFCC and Neural Network

Cited by 11 publications

References 4 publications

Research on Feature Extraction of Underwater Acoustic Target Radiation Noise Based on Machine Learning Algorithm

Research on Feature Extraction of Underwater Acoustic Target Radiation Noise Based on Machine Learning Algorithm

Classification of Transient Signals in Ocean Background Noise Using Bayesian Classifier

Underwater Acoustic Target Recognition: A Combination of Multi-Dimensional Fusion Features and Modified Deep Neural Network

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