• Title/Summary/Keyword: Underwater transient signal

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Underwater transient signal detection based on CFAR Power-Law using Doubel-Density Discerte Wavelet Transform coefficient (Double-Density 이산 웨이블렛 변환의 계수를 이용한 CFAR Power-Law기반의 수중 천이 신호 탐지)

  • Jung, Seung-Taek;Cha, Dae-Hyun;Lim, Tae-Gyun;Kim, Jong-Hoon;Hwang, Chan-Sik
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2007.10a
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    • pp.175-179
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    • 2007
  • To existing method which uses energy variation and spectrum deviation to detect the underwater transient signal is useful to detect white noise environment, but it is not useful to do colored noise environment. To improve capacity of detecting the underwater transient signal both in white noise environment and colored noise environment, this study takes advantage of Double Density Discrete Wavelet Transform and CFAR Power-Law.

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Underwater Transient Signal Detection Using Higher-order Statistics and Wavelet Analysis (고차통계 기법과 웨이브렛을 이용한 수중 천이신호 탐지)

  • 조환래;오선택;오택환;나정열
    • The Journal of the Acoustical Society of Korea
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    • v.22 no.8
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    • pp.670-679
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    • 2003
  • This paper deals with application of wavelet transform, which is known to be good for time-frequency analysis, in order to detect the underwater transient signals embedded in ambient noise. A new detector of acoustic transient signals is presented. It combines two detection tools: wavelet analysis and higher-order statistics. Using both techniques, the detection of the transient signal is possible in low signal to noise ratio condition. The proposed algorithm uses the wavelet transform of a partition of the signal on frequency domain, and then higher-order statistics tests the Gaussian nature of the segments.

Feature Extraction Algorithm for Underwater Transient Signal Using Cepstral Coefficients Based on Wavelet Packet (웨이브렛 패킷 기반 캡스트럼 계수를 이용한 수중 천이신호 특징 추출 알고리즘)

  • Kim, Juho;Paeng, Dong-Guk;Lee, Chong Hyun;Lee, Seung Woo
    • Journal of Ocean Engineering and Technology
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    • v.28 no.6
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    • pp.552-559
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    • 2014
  • In general, the number of underwater transient signals is very limited for research on automatic recognition. Data-dependent feature extraction is one of the most effective methods in this case. Therefore, we suggest WPCC (Wavelet packet ceptsral coefficient) as a feature extraction method. A wavelet packet best tree for each data set is formed using an entropy-based cost function. Then, every terminal node of the best trees is counted to build a common wavelet best tree. It corresponds to flexible and non-uniform filter bank reflecting characteristics for the data set. A GMM (Gaussian mixture model) is used to classify five classes of underwater transient data sets. The error rate of the WPCC is compared using MFCC (Mel-frequency ceptsral coefficients). The error rates of WPCC-db20, db40, and MFCC are 0.4%, 0%, and 0.4%, respectively, when the training data consist of six out of the nine pieces of data in each class. However, WPCC-db20 and db40 show rates of 2.98% and 1.20%, respectively, while MFCC shows a rate of 7.14% when the training data consists of only three pieces. This shows that WPCC is less sensitive to the number of training data pieces than MFCC. Thus, it could be a more appropriate method for underwater transient recognition. These results may be helpful to develop an automatic recognition system for an underwater transient signal.

Extraction of an Underwater Transient Signal Using Sound Mask-filter (사운드 마스크 필터를 이용한 수중 과도 신호 추출)

  • Bok, Tae-Hoon;Kim, Juho;Paeng, Dong-Guk;Lee, Chong Hyun;Bae, Jinho;Kim, Seongil
    • The Journal of the Acoustical Society of Korea
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    • v.31 no.8
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    • pp.532-541
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    • 2012
  • An underwater transient signal is distinguished from an ambient noise. Database for the underwater transient signal is required since the underwater transient signal shows various characteristics depending on acoustic features. In the paper, hence, sound mask-filter was applied to extract the transient signals which exist temporally and locally in the ocean. The standard signal was chosen and cross-correlated with the raw signal. A mask-filter for a transient signal was obtained using the threshold which was decided by the maximum likelihood method in the envelope of the cross-correlated signal. Using the sound mask-filter, the transient signal of a sea catfish {Galeichthys felis (Linnaeus)} was extracted from the underwater ambient noise. Similarly, the man-made signal was added into the noise and it was extracted by the same method. We also have demonstrated the significance of the transient signal through comparing the extracted signals depending on the standard signal. In the results, the proposed method, sound mask-filtering, could be utilized as a database construction of the transient signals in underwater noise. Particularly, this study would be useful to extract the wanted signal from arbitrary 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.

Feature Extraction and Classification of Underwater Transient Signal using MFCC and Wavelet Packet Based on Entropy (MFCC과 엔트로피 기반의 웨이블릿 패킷 기법을 이용한 수중 천이신호의 특징추출 및 식별)

  • Jung, Jae-Gun;Park, Jeong-Hyun;Kim, Dong-Wook;Hwang, Chan-Sik
    • Proceedings of the KAIS Fall Conference
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    • 2009.05a
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    • pp.781-784
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    • 2009
  • 본 논문에서는 실제 수중 환경에서 선박 또는 잠수함으로부터 발생하는 인위적인 천이신호와 돌고래, 새우 등의 해양 생물로부터 발생하는 천이신호들을 식별하기 위한 특징벡터 추출 기법을 제안하였다. MFCC와 엔트로피 기반의 웨이블릿 패킷 기법을 이용하여 특징을 추출하고, 이 두 특징들을 동시에 적용하여 수중 천이신호를 식별하고자 한다. 기존의 방법인 MFCC와 웨이블릿 패킷 기법과 이 두 방법을 동시에 적용했을 때의 식별률을 비교하였고, 전방향 신경회로망(feed-forward neural network)을 그 특징벡터의 성능을 평가하기 위한 식별기로 사용하였다.

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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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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.

Detection of Underwater Transient Signals Using Noise Suppression Module of EVRC Speech Codec (EVRC 음성부호화기의 잡음억제단을 이용한 수중 천이신호 검출)

  • Kim, Tae-Hwan;Bae, Keun-Sung
    • The Journal of the Acoustical Society of Korea
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    • v.26 no.6
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    • pp.301-305
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    • 2007
  • In this paper, we propose a simple algorithm for detecting underwater transient signals on the fact that the frequency range of underwater transient signals is similar to audio frequency. For this, we use a preprocessing module of EVRC speech codec that is the standard speech codec of the mobile communications. If a signal is entered into EVRC noise suppression module, we can get some parameters such as the update flag, the energy of each channel, the noise suppressed signal, the energy of input signal, the energy of background noise, and the energy of enhanced signal. Therefore the energy of the enhanced signal that is normalized with the energy of the background noise is compared with the pre-defined detection threshold, and then we can detect the transient signal. And the detection threshold is updated using the previous value in the noisy period. The experimental result shows that the proposed algorithm has $0{\sim}4% error rate in the AWGN or the colored noise environment.

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.