• Journal of IKEEE
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• v.24 no.1
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• pp.339-346
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• 2020

In this paper, we propose an angle estimator that is designed to be applied to Bluetooth low-power application technology based on multiple signal classification (MUSIC) algorithm, and present the result of implementation in FPGA. The MUSIC algorithm is designed for H/W high-speed design because it requires a lot of calculations due to high accuracy, and the snapshot variable is designed to cope with various resolution requirements of indoor systems. As a result of the implementation with Xilinx zynq-7000, it was confirmed that 9,081 LUTs were implemented, and it was designed to operate at =the operating frequency of 100MHz.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.3
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• pp.291-297
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• 2016

In this paper, we are comparative analysis of exit algorithm and proposal algorithm for desired target direction of arrival estimation in correlation signal system. Proposed algorithm in this paper is to decrease target direction of arrival an estimation error probability using bayesian, maximum posterior, and MUSIC algorithm in order to decrease direction of arrival error probability as optimize and use linear constrained minimum variance to update weight value. Through simulation, we were comparative analysis proposed algorithm and exit MUSIC algorithm. In case SNR is 10dB and antenna element arrays are 9 and 12, We show the superior performance of the proposed method relative to the class method to decrease of signal estimation error probability about 11% and 13%, respectively.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.5
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• pp.251-257
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• 2014

Digital RDA system is retransmit into the opposite direction of the incident signals. Digital RDA system have a disadventage that this system do not signal classification in multipath environment. because multipath signal is shown as vector sum of multipath signal, digital RDA system required complex signal process for multipath signal classification. In this paper, to solve these problem we propose hybrid digital RDA system which combination of the MUSIC algorithm and the digital RDA system. Proposed system has two modes. First mode is digital RDA mode. Secornd mode is digital beamforming mode. Digital RDA mode is used in situations where the less the impact of multipath. Digital beamforming mode is applied to multipath effects is greater. In secornd mode, we find optimal path using MUSIC algorithm. After than the proposed system uses only the optimal path. Through the proposed system in a multipath environment with digital RDA can be used to supplement a disadvantage.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.4
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• pp.773-778
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• 2020

In this paper, we analyzed the effects of musical stimuli on humans in performing mathematical tasks through EEG measurements. The musical stimuli were divided into preferred music and non-preferred music, and mathematical tasks were divided into memorization task and procedure task. The data measured in the EEG experiments was divided into frequency bands of Theta, SMR, and Mid-beta because of the concentration. In our results, preferred music causes more positive emotional response than no music and non-preferred music regardless of the type of mathematical task.

• The Journal of the Acoustical Society of Korea
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• v.38 no.6
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• pp.697-702
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• 2019

We propose a multi-band multi-scale DenseNet with dilated convolution that separates background music signals from broadcast content. Dilated convolution can learn the multi-scale context information represented by spectrogram. In computer simulation experiments, the proposed architecture is shown to improve Signal to Distortion Ratio (SDR) by 0.15 dB and 0.27 dB in 0dB and -10 dB Signal to Noise Ratio (SNR) environments, respectively.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.19 no.3
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• pp.294-303
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• 2008

ISAR(Inverse Synthetic Aperture Radar) images represent the 2-D(two-dimensional) spatial distribution of RCS (Radar Cross Section) of an object, and they can be applied to the problem of target identification. A traditional approach to ISAR imaging is to use a 2-D IFFT(Inverse Fast Fourier Transform). However, the 2-D IFFT results in low resolution ISAR images especially when the measured frequency bandwidth and angular region are limited. In order to improve the resolution capability of the Fourier transform, various high-resolution spectral estimation approaches have been applied to obtain ISAR images, such as AR(Auto Regressive), MUSIC(Multiple Signal Classification) or Modified MUSIC algorithms. In this study, these high-resolution spectral estimators as well as 2-D IFFT approach are combined with a recently developed ISAR image classification algorithm, and their performances are carefully analyzed and compared in the framework of radar target recognition.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.1
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• pp.81-87
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• 2013

In this paper, we propose a music search algorithm for automotive infotainment system. The proposed method extracts fingerprints using the high peaks based on log-spectrum of the music signal, and the extracted music fingerprints store in cloud server applying a hash value. In the cloud server, the most similar music is retrieved by comparing the user's query music with the fingerprints stored in hash table of cloud server. To evaluate the performance of the proposed music search algorithm, we measure an accuracy of the retrieved results according to various length of the query music and measure a retrieval time according to the number of stored music database in hash table.

• The Journal of the Acoustical Society of Korea
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• v.41 no.2
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• pp.115-121
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• 2022

In this paper, we propose a music classification system according to user emotions using Electroencephalogram (EEG) features that appear when listening to music. In the proposed system, the relationship between the emotional EEG features extracted from EEG signals and the auditory features extracted from music signals is learned through a deep regression neural network. The proposed system based on the regression model automatically generates EEG features mapped to the auditory characteristics of the input music, and automatically classifies music by applying these features to an attention-based deep neural network. The experimental results suggest the music classification accuracy of the proposed automatic music classification framework.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.5
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• pp.239-246
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• 2023

In this paper, we propose an open-loop algorithm to classify speech and music signals using the spectral flux parameters and Mel Frequency Cepstral Coefficients(MFCC) parameters for the audio coder. To increase responsiveness, the MFCC was used as a short-term feature parameter and spectral fluxes were used as a long-term feature parameters to improve accuracy. The overall voice/music signal classification decision is made by combining the short-term classification method and the long-term classification method. The Gaussian Mixed Model (GMM) was used for pattern recognition and the optimal GMM parameters were extracted using the Expectation Maximization (EM) algorithm. The proposed long-term and short-term combined speech/music signal classification method showed an average classification error rate of 1.5% on various audio sound sources, and improved the classification error rate by 0.9% compared to the short-term single classification method and 0.6% compared to the long-term single classification method. The proposed speech/music signal classification method was able to improve the classification error rate performance by 9.1% in percussion music signals with attacks and 5.8% in voice signals compared to the Unified Speech Audio Coding (USAC) audio classification method.

• Journal of Navigation and Port Research
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• v.36 no.10
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• pp.833-838
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• 2012

TDOA which is one of the position estimation methods is used on indoor positioning, jammer localization, rescue of life, etc. due to high accuracy and simple structure. This paper proposes the super-resolution TDOA estimator using MPM(Matrix Pencil Method). The proposed estimator has more accuracy and is applicable to narrowband signal compared with the conventional cross-correlation. Furthermore, its complexity is low because obtained data directly is used for construction of matrix unlike the MUSIC(Multiple Signal Classification) which is one of the well-known super-resolution estimator using covariance matrix. To validate the performance of proposed estimator, errors of estimation and computational burden is compared to MUSIC through software simulation.