• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.11
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• pp.2131-2138
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• 2011

GPS is vulnerable to jamming because of extremely low signal power. Many anti-jamming techniques are studied for complement this vulnerability. Anti-jamming techniques using array antenna are most effective technique and these techniques are required the DOA estimates. MUSIC algorithm and Root-MUSIC Algorithm are typical algorithms used in DOA estimation. Two algorithms have different characteristics, so the choice of an algorithm may depends on many factors such as the environment and the system requirements. The analysis and performance comparison of both algorithms is necessary to choose the best method to apply. This paper summarizes the theory of MUSIC and Root-MUSIC algorithms. And this paper extends both algorithm to estimate two-dimensional angles. The software simulator of both algorithms are implemented to evaluate the performance. Root-MUSIC algorithm has the computational advantage on ULA. MUSIC algorithm is applicable to any antenna array. MUSIC shows better estimation performance when number of array element is small while the computational load of MUSIC is much higher than Root-MUSIC.

• Journal of IKEEE
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• v.14 no.4
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• pp.305-311
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• 2010

Most recent web search engines perform effective keyword-based multimedia contents retrieval by investigating keywords associated with multimedia contents on the Web and comparing them with query keywords. On the other hand, most music and compilation albums provide professional artwork as cover art that will be displayed when the music is played. If the cover art is not available, then the music player just displays some dummy or random images, but this has been a source of dissatisfaction. In this paper, in order to automatically create cover art that is matched with music contents, we propose a music album cover art creation scheme based on music contents analysis and result synthesis. We first (i) analyze music contents and their lyrics and extract representative keywords, (ii) expand the keywords using WordNet and generate various queries, (iii) retrieve related images from the Web using those queries, and finally (iv) synthesize them according to the user preference for album cover art. To show the effectiveness of our scheme, we developed a prototype system and reported some results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.2
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• pp.467-479
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• 2022

Working memory (WM), which plays a vital role in daily activities, is a memory system that temporarily stores and processes information when people are engaged in complex cognitive activities. The influence of music on WM has been widely studied. In this work, we conducted a series of n-back memory experiments with different task difficulties and multiple trials on 14 subjects under the condition of no music and Alpha wave leading music. The analysis of behavioral data show that the change of music condition has significant effect on the accuracy and time of memory reaction (p<0.01), both of which are improved after the stimulation of Alpha wave music. Behavioral results also suggest that short-term training has no significant impact on working memory. In the further analysis of electrophysiology (EEG) data recorded in the experiment, auto-regressive (AR) model is employed to extract features, after which an average classification accuracy of 82.9% is achieved with support vector machine (SVM) classifier in distinguishing between before and after WM enhancement. The above findings indicate that Alpha wave leading music can improve WM, and the combination of AR model and SVM classifier is effective in detecting the brain activity changes resulting from music stimulation.

• The Journal of the Acoustical Society of Korea
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• v.26 no.7
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• pp.334-342
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• 2007

This paper presents an algorithm for the automatic detection of chord names, scales and tonalities from music file, expressed in MusicXML format which has enough information to determine harmonies vertically like 4-part choir. Chord names are absolute names which can be used and analysed independently of the tonality An algorithm selecting the best chord name is described, which can decide the most appropriate one from ambiguous situations. Candidate musical scales are extracted using the notes in a given time window. The tonalities of the music are determined using the chord names and candidate scales. The final output format of the process is also MusicXML file with chord names, marked non-harmonic notes, relative harmonic symbols and tonalities.

• Journal of Biomedical Engineering Research
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• v.29 no.4
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• pp.329-336
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• 2008

Recently, causality analysis of source time series extracted from EEG or MEG signals is becoming of great importance in human brain mapping studies and noninvasive diagnosis of various brain diseases. Two approaches have been widely used for the analyses: one is independent component analysis (ICA), and the other is multiple signal classification (MUSIC). To the best of our knowledge, however, any comparison studies to reveal the difference of the two approaches have not been reported. In the present study, we compared the performance of the two different techniques, ICA and MUSIC, especially focusing on how accurately they can estimate and separate various brain electrical signals such as linear, nonlinear, and chaotic signals without a priori knowledge. Results of the realistic simulation studies, adopting directed transfer function (DTF) and Granger causality (GC) as measures of the accurate extraction of source time series, demonstrated that the MUSIC-based approach is more reliable than the ICA-based approach.

• Journal of Korea Multimedia Society
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• v.21 no.1
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• pp.45-50
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• 2018

This research paper describes how to summarize music lyrics using the TextRank algorithm. This method can summarize music lyrics as important lyrics. Therefore, we recommend music more effectively than analyzing the number of words and recommending music.

• Journal of Korea Multimedia Society
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• v.19 no.5
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• pp.826-836
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• 2016

Mastering a musical instrument for an unskilled beginning learner is not an easy task. It requires playing every note correctly and maintaining the tempo accurately. Any music comes in two forms, a music score and it rendition into an audio music. The proposed method of assisting beginning music players in both aspects employs two popular pattern recognition methods for audio-visual analysis; they are support vector machine (SVM) for music score recognition and hidden Markov model (HMM) for audio music performance tracking. With proper synchronization of the two results, the proposed music learning assistant system can give useful feedback to self-training beginners.

• The Journal of the Korea Contents Association
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• v.16 no.11
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• pp.456-463
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• 2016

This study investigates the relationship between the digital music service acceptance factors on the Perceived value, and Customer Satisfaction. Remarkable points of this study are as follows. First, according to the result of the analysis of relationship between digital music service acceptance factors and perceived value, diversity of digital music products and quality of digital music system affects significantly on the perceived value. but, usability of digital music service use does not have an effect on the perceived value. Second, as the result of analysis of the relationship between digital music service acceptance factors and customer satisfaction, diversity of digital music products, usability of digital music service and quality of digital music system affects significantly on the perceived value. These finding should assist digital music marketers and academics in their understanding of building loyalty on digital music service.

• Journal of the Korea Society of Computer and Information
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• v.29 no.5
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• pp.177-187
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• 2024

177-Existing music recommendation systems do not sufficiently consider the discrepancy between the intended emotions conveyed by song lyrics and the actual emotions felt by users. In this study, we generate topic vectors for lyrics and user comments using the LDA model, and construct a user preference model by combining user behavior trajectories reflecting time decay effects and playback frequency, along with statistical characteristics. Empirical analysis shows that our proposed model recommends music with higher accuracy compared to existing models that rely solely on lyrics. This research presents a novel methodology for improving personalized music recommendation systems by integrating emotion recognition and user behavior analysis.

• Journal of Korea Multimedia Society
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• v.16 no.11
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• pp.1325-1337
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• 2013

Music affects sensitivity and emotion of human, emotional power of the music has been applied to various fields. Especially, to visualize as well as listen to music is able to create various atmosphere. In this paper, we proposed sensitivity control system for interaction with people to merge music and lighting. Because existing FT(Fourier Transform) has not information about the time, to analyze information of changed signal according to the time is difficult. In order to solve such a problem, we use STFT(Short Time Fourier Transform) method to analyze music signal. and also, we classified music for three genre and compared the frequency characteristics according to genre, and control the color, brightness of LED light based on the frequency components within analysis range. Unlike existing LED lighting control study using music, we had color control of emotional lighting and brightness control using variation amount of music signal in this paper. Proposed lighting control system will be able to utilize various industry fields as well as emotional lighting.