• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.2
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• pp.114-120
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• 1992

In this paper we derived the asymptotic distribution of the MUSIC null-spectrum, form which an exact expression of the asymptotic variance of the MUSIC null-spectrum can be obtained. From this result in addition an explicit expression of the normalized standard deviation has been derived and it is shown that the normalized standard deviation depends only on the number of sensors and the number of signals.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1991.10a
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• pp.115-118
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• 1991

In this paper we derived the asymptotic distribution of the MUSIC null-spectrum, from which an exact expression of the asymptotic variance of the MUSIC null-spectrum can be obtained. From this result in addition an explicit expression of the normalized standard deviation (NSD) has been derived and it is shown that the NSD is affected by the number of sensors and the number of signals.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.12C
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• pp.1156-1163
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• 2007

A digital formatted music can be summarized with a fixed length using spectrum signal processing in this paper. We experimentally tested the hypothesis that the power spectrum of a popular music has $\frac{1}{\Large f}$ shape. Based on this hypothesis, a music is summarized by a system proposed in the paper. The system consists of a pre-processing block obtaining a test spectrum and a decision block calculating similarities. It is noteworthy that a digital formatted music can be summarized automatically using a similar system based on various hypotheses.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.15 no.3
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• pp.223-230
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• 2004

A method for measuring array manifold which is the array antenna response of incident signal is presented. Array manifold measurement procedure by the presented method is explained for UCA(Uniform Circular Array), and spatial spectrum of 300 ㎒ tone signal incident on UCA is estimated by MUSIC algorithm in which spatial spectrum peak is searched with measured array manifold. Spatial spectrum estimation using array manifold measured by the proposed method shows superior performance to calculated array manifold.

• The Journal of the Acoustical Society of Korea
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• v.26 no.5
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• pp.207-213
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• 2007

In this research, we propose an efficient Speech/Music discrimination method that uses spectrum analysis and neural network. The proposed method extracts the duration feature parameter(MSDF) from a spectral peak track by analyzing the spectrum, and it was used as a feature for Speech/Music discriminator combined with the MFSC. The neural network was used as a Speech/Music discriminator, and we have reformed various experiments to evaluate the proposed method according to the training pattern selection, size and neural network architecture. From the results of Speech/Music discrimination, we found performance improvement and stability according to the training pattern selection and model composition in comparison to previous method. The MSDF and MFSC are used as a feature parameter which is over 50 seconds of training pattern, a discrimination rate of 94.97% for speech and 92.38% for music. Finally, we have achieved performance improvement 1.25% for speech and 1.69% for music compares to the use of MFSC.

• Journal of the Korea Society of Computer and Information
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• v.22 no.1
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• pp.9-14
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• 2017

In this paper, we propose that the contrast features of octave spectrum can be used to show spectral contrast features of some music clips. It shows the relative spectral distribution rather than average spectrum. From the experiment, it can be seen the method of spectral contrast features has a good performance in classification of music styles. Another comparative experiment shows that the method of spectral contrast features can better distinguish different music styles than the method of MFCC features that commonly used previously in the classification system of music styles.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.6 no.2
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• pp.95-106
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• 2007

This paper proposes an effective tempo feature extraction method for music information retrieval. The tempo information is modeled by the narrow-band temporal modulation components, which are decomposed into a modulation spectrum via joint frequency analysis. In implementation, the tempo feature is directly extracted from the modified discrete cosine transform coefficients, which is the output of partial MP3(MPEG 1 Layer 3) decoder. Then, different features are extracted from the amplitudes of modulation spectrum and applied to different music information retrieval tasks. The logarithmic scale modulation frequency coefficients are employed in automatic music emotion classification and music genre classification. The classification precision in both systems is improved significantly. The bit vectors derived from adaptive modulation spectrum is used in audio fingerprinting task That is proved to be able to achieve high robustness in this application. The experimental results in these tasks validate the effectiveness of the proposed tempo feature.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.8 no.5
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• pp.400-404
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• 2015

In this paper, we study for direction of arrival MUSIC spatial spectrum algorithm in order to desired signal estimation in spatial. Proposal MUSIC spatial spectrum algorithm in paper use model error and Bayesian method to estimation on correct target position. Receiver array response vector using adaptive array antenna use Bayesian method, and target position estimate to update weight value with model error method. Target's signal estimation of desired direction of arrival in this paper apply weight value of signal covariance matrix for array response vector after removing incident signal interference and noise, respectively. Though simulation, we analyze to compare proposed method with general method.

• The Journal of the Acoustical Society of Korea
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• v.25 no.2E
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• pp.69-73
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• 2006

In this paper, we propose a robust music audio fingerprinting system for automatic music retrieval. The fingerprint feature is extracted from the long-term dynamic modulation spectrum (LDMS) estimation in the perceptual compressed domain. The major advantage of this feature is its significant robustness against severe background noise from the street and cars. Further the fast searching is performed by looking up hash table with 32-bit hash values. The hash value bits are quantized from the logarithmic scale modulation frequency coefficients. Experiments illustrate that the LDMS fingerprint has advantages of high scalability, robustness and small fingerprint size. Moreover, the performance is improved remarkably under the severe recording-noise conditions compared with other power spectrum-based robust fingerprints.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.3
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• pp.212-218
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• 2013

This paper presents an SVD(Singular Value Decomposition)-Pseudo Spectrum method for SAR (Synthetic Aperture Radar) imaging. The purpose of this work is to improve resolution and target separability of SAR images. This paper proposes SVD-Pseudo Spectrum method whose advantages are noise robustness, reduction of sidelobes and high resolution of spectral estimation. SVD-Pseudo Spectrum method uses Hankel Matrix of signal components and SVD (Singular Value Decomposition) method. In this paper, it is demonstrated that the SVD-Pseudo Spectrum method shows better performance than the matched filtering method and the conventional super-resolution based multiple signal classification (MUSIC) method in SAR image processing. The targets to be separated are modeled, and this modeled data is used to demonstrate the performance of algorithms.