• Title/Summary/Keyword: Underwater transient signal classification

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Classification of Underwater Transient Signals Using Gaussian Mixture Model (정규혼합모델을 이용한 수중 천이신호 식별)

  • Oh, Sang-Hwan;Bae, Keun-Sung
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.16 no.9
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    • pp.1870-1877
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    • 2012
  • Transient signals generally have short duration and variable length with time-varying and non-stationary characteristics. Thus frame-based pattern matching method is useful for classification of transient signals. In this paper, we propose a new method for classification of underwater transient signals using a Gaussian mixture model(GMM). We carried out classification experiments for various underwater transient signals depending upon the types of noise, signal-to-noise ratio, and number of mixtures in the GMM. Experimental results have verified that the proposed method works quite well for classification of underwater transient signals.

Underwater Transient Signal Classification Using Eigen Decomposition Based on Wigner-Ville Distribution Function (위그너-빌 분포 함수 기반의 고유치 분해를 이용한 수중 천이 신호 식별)

  • Bae, Keun-Sung;Hwang, Chan-Sik;Lee, Hyeong-Uk;Lim, Tae-Gyun
    • The Journal of the Acoustical Society of Korea
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    • v.26 no.3
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    • pp.123-128
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    • 2007
  • This Paper Presents new transient signal classification algorithms for underwater transient signals. In general. the ambient noise has small spectral deviation and energy variation. while a transient signal has large fluctuation. Hence to detect the transient signal, we use the spectral deviation and power variation. To classify the detected transient signal. the feature Parameters are obtained by using the Wigner-Ville distribution based eigenvalue decomposition. The correlation is then calculated between the feature vector of the detected signal and all the feature vectors of the reference templates frame-by-frame basis, and the detected transient signal is classified by the frame mapping rate among the class database.

Classification of Underwater Transient Signals Using MFCC Feature Vector (MFCC 특징 벡터를 이용한 수중 천이 신호 식별)

  • Lim, Tae-Gyun;Hwang, Chan-Sik;Lee, Hyeong-Uk;Bae, Keun-Sung
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.32 no.8C
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    • pp.675-680
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    • 2007
  • This paper presents a new method for classification of underwater transient signals, which employs frame-based decision with Mel Frequency Cepstral Coefficients(MFCC). The MFCC feature vector is extracted frame-by-frame basis for an input signal that is detected as a transient signal, and Euclidean distances are calculated between this and all MFCC feature. vectors in the reference database. Then each frame of the detected input signal is mapped to the class having minimum Euclidean distance in the reference database. Finally the input signal is classified as the class that has maximum mapping rate in the reference database. Experimental results demonstrate that the proposed method is very promising for classification of underwater transient signals.

Vector Quantization of Reference Signals for Efficient Frame-Based Classification of Underwater Transient Signals (프레임 기반의 효율적인 수중 천이신호 식별을 위한 참조 신호의 벡터 양자화)

  • Lim, Tae-Gyun;Kim, Tae-Hwan;Bae, Keun-Sung;Hwang, Chan-Sik
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.34 no.2C
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    • pp.181-185
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    • 2009
  • When we classify underwater transient signals with frame-by-frame decision, a database design method for reference feature vectors influences on the system performance such as size of database, computational burden and recognition rate. In this paper the LBG vector quantization algorithm is applied to reduction of the number of feature vectors for each reference signal for efficient classification of underwater transient signals. Experimental results have shown that drastic reduction of the database size can be achieved while maintaining the classification performance by using the LBG vector quantization.

Frame Based Classification of Underwater Transient Signal Using MFCC Feature Vector and Neural Network (MFCC 특징벡터와 신경회로망을 이용한 프레임 기반의 수중 천이신호 식별)

  • Lim, Tae-Gyun;Kim, Il-Hwan;Kim, Tae-Hwan;Bae, Keun-Sung
    • Proceedings of the IEEK Conference
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    • 2008.06a
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    • pp.883-884
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    • 2008
  • This paper presents a method for classification of underwater transient signals using, which employs a binary image pattern of the mel-frequency cepstral coefficients(MFCC) as a feature vector and a neural network as a classifier. A feature vector is obtained by taking DCT and 1-bit quantization for the square matrix of the MFCC sequences. The classifier is a feed-forward neural network having one hidden layer and one output layer, and a back propagation algorithm is used to update the weighting vector of each layer. Experimental results with some underwater transient signals demonstrate that the proposed method is very promising for classification of underwater transient signals.

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Classification of Transient Signals in Ocean Background Noise Using Bayesian Classifier (베이즈 분류기를 이용한 수중 배경소음하의 과도신호 분류)

  • Kim, Ju-Ho;Bok, Tae-Hoon;Paeng, Dong-Guk;Bae, Jin-Ho;Lee, Chong-Hyun;Kim, Seong-Il
    • Journal of Ocean Engineering and Technology
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    • v.26 no.4
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    • pp.57-63
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    • 2012
  • 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.

A Study on the Reference Template Database Design Method for Frame-based Classification of Underwater Transient Signals (프레임 기반의 수중 천이신호 식별을 위한 기준패턴의 데이터베이스 구성 방법에 관한 연구)

  • Lim, Tae-Gyun;Ryu, Jong-Youb;Kim, Tae-Hwan;Bae, Keun-Sung
    • Proceedings of the IEEK Conference
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    • 2008.06a
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    • pp.885-886
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    • 2008
  • This paper presents a reference template design method for frame-based classification of underwater transient signals. In the proposed method, framebased feature vectors of each reference signal are clustered by using LBG clustering algorithm to reduce the number of feature vectors in each class. Experimental results have shown that drastic reduction of the reference database can be achieved while maintaining the classification performance with LBG clustering algorithm.

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Feature Vector Extraction and Automatic Classification for Transient SONAR Signals using Wavelet Theory and Neural Networks (Wavelet 이론과 신경회로망을 이용한 천이 수중 신호의 특징벡타 추출 및 자동 식별)

  • Yang, Seung-Chul;Nam, Sang-Won;Jung, Yong-Min;Cho, Yong-Soo;Oh, Won-Tcheon
    • The Journal of the Acoustical Society of Korea
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    • v.14 no.3
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    • pp.71-81
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    • 1995
  • In this paper, feature vector extraction methods and classification algorithms for the automatic classification of transient signals in underwater are discussed. A feature vector extraction method using wavelet transform, which shows good performance with small number of coefficients, is proposed and compared with the existing classical methods. For the automatic classification, artificial neural networks such as multilayer perceptron (MLP), radial basis function (RBF), and MLP-Class are utilized, where those neural networks as well as extracted feature vectors are combined to improve the performance and reliability of the proposed algorithm. It is confirmed by computer simulation with Traco's standard transient data set I and simulated data that the proposed feature vector extraction method and classification algorithm perform well, assuming that the energy of a given transient signal is sufficiently larger than that of a ambient noise, that there are the finite number of noise sources, and that there does not exist noise sources more than two simultaneously.

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Binary Tree Architecture Design for Support Vector Machine Using Dynamic Time Warping (DTW를 이용한 SVM 기반 이진트리 구조 설계)

  • Kang, Youn Joung;Lee, Jaeil;Bae, Jinho;Lee, Seung Woo;Lee, Chong Hyun
    • Journal of the Institute of Electronics and Information Engineers
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    • v.51 no.6
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    • pp.201-208
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    • 2014
  • In this paper, we propose the classifier structure design algorithm using DTW. Proposed algorithm uses DTW result to design the binary tree architecture based on the SVM which classify the multi-class data. Design the binary tree architecture for Support Vector Machine(SVM-BTA) using the threshold criterion calculated by the sum columns in square matrix which components are the reference data from each class. For comparison the performance of the proposed algorithm, compare the results of classifiers which binary tree structure are designed based on database and k-means algorithm. The data used for classification is 333 signals from 18 classes of underwater transient noise. The proposed classifier has been improved classification performance compared with classifier designed by database system, and probability of detection for non-biological transient signal has improved compare with classifiers using k-means algorithm. The proposed SVM-BTA classified 68.77% of biological sound(BO), 92.86% chain(CHAN) the mechanical sound, and 100% of the 6 kinds of the other classes.

Feature Vector Extraction Method for Transient Sonar Signals Using PR-QMF Wavelet Transform (PR-QMF Wavelet Transform을 이용한 천이 수중 신호의 특징벡타 추출 기법)

  • Jung, Yong-Min;Choi, Jong-Ho;Cho, Yong-Soo;Oh, Won-Tcheon
    • The Journal of the Acoustical Society of Korea
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    • v.15 no.1
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    • pp.87-92
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    • 1996
  • Transient signals in underwater show several characterisrics, that is, short duration, strong nonstationarity, various types of transient sources, which make it difficult to analyze and classify transient signals. In this paper, the feature vector extraction method for transient SOMAR signals is discussed by applying digital signal processing methods to the analysis of transient signals. A feature vector extraction methods using wavelet transform, which enable us to obtain better recognition rate than automatic classification using the classical method, are proposed. It is confirmed by simulation that the proposed method using wavelet transform performs better than the classical method even with smaller number of feature vectors. Especially, the feature vector extraction method using PR-QMF wavelet transform with the Daubechies coefficients is shown to perform well in noisy environment with easy implementation.

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