• Journal of Advanced Marine Engineering and Technology
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• v.36 no.8
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• pp.1129-1135
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• 2012

To employ NMF to transcribe music by extracting feature matrix and weight matrix at the same time, it is necessary to know in advance the dimension of the feature matrix, and to determine the pitch of each extracted feature vector. Another drawback of this approach is that it becomes more difficult to accurately extract the feature matrix as the number of pitches included in the target music increases. In this study, we prepare a feature matrix database, and apply the matrix to transcribe real music. Transcription experiments are conducted by applying the feature matrix to the music played on the same piano on which the feature matrix is extracted, as well as on the music played on another piano. These results are also compared to those of another experiment where the feature matrix and weight matrix are extracted simultaneously, without making use of the database. We could observe that the proposed method outperform the method in which the two matrices are extracted at the same time.

• Journal of Broadcast Engineering
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• v.9 no.1
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• pp.2-7
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• 2004

In this paper, we proposed the content-based music information retrieval technique using some MPEG-7 low-level descriptors. Especially, pitch information and timbral features can be applied in music genre classification, music retrieval, or QBH(Query By Humming) because these can be modeling the stochasticpattern or timbral information of music signal. In this work, we restricted the music domain as O.S.T of movie or soap opera to apply broadcasting system. That is, the user can retrievalthe information of the unknown music using only an audio clip with a few seconds extracted from video content when background music sound greeted user's ear. We proposed the audio feature set organized by MPEG-7 descriptors and distance function by vector distance or ratio computation. Thus, we observed that the feature set organized by pitch information is superior to timbral spectral feature set and IFCR(Intra-Feature Component Ratio) is better than ED(Euclidean Distance) as a vector distance function. To evaluate music recognition, k-NN is used as a classifier

• Journal of the Korea Society of Computer and Information
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• v.15 no.11
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• pp.91-99
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• 2010

Recently, recognizing user resource for personalized service has been needed in digital content service fields. Especially, to analyze user taste, recommend music and service music related information need recognition of user music file in case of online music service. Music related information service is offered through recognizing user music based on tag information. Recognition error has grown by weak points like changing and removing of tag information. Techniques of content based user music recognition with music signal itself are researched for solving upper problems. In this paper, we propose user music recognition on the internet by extracted feature from music signal. Features are extracted after suitable preprocessing for structure of content based user music recognition. Recognizing on music server consist of feature form are progressed with extracted feature. Through this, user music can be recognized independently of tag data. 600 music was collected and converted to each 5 music qualities for proving of proposed recognition. Converted 3000 experiment music on this method is used for recognition experiment on music server including 300,000 music. Average of recognition ratio was 85%. Weak points of tag based music recognition were overcome through proposed content based music recognition. Recognition performance of proposed method show a possibility that can be adapt to online music service in practice.

• 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 KIISE:Software and Applications
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• v.35 no.11
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• pp.692-700
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• 2008

In some previous works on musical genre classification, human experts specify segments of a song for extracting musical features. Although this approach might contribute to performance enhancement, it requires manual intervention and thus can not be easily applied to new incoming songs. To extract musical features without the manual intervention, most of recent researches on music genre classification extract features from a pre-determined part of a song (for example, 30 seconds after initial 30 seconds), which may cause loss of accuracy. In this paper, in order to alleviate the accuracy problem, we propose a new method, which extracts features from representative segments (or main theme part) identified by structure analysis of music piece. The proposed method detects segments with repeated melody in a song and selects representative ones among them by considering their positions and energies. Experimental results show that the proposed method significantly improve the accuracy compared to the approach using a pre-determined part.

• Journal of Internet Computing and Services
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• v.16 no.6
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• pp.133-141
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• 2015

Mass multimedia contents are shared through various social media servies including social network service. As social network reveals user's current situation and interest, highly satisfactory personalized recommendation can be made when such features are applied to the recommendation system. In addition, classifying the music by emotion and using analyzed information about user's recent emotion or current situation by analyzing user's social network, it will be useful upon recommending music to the user. In this paper, we propose a music recommendation method that makes an emotion model to classify the music, classifies the music according to the emotion model, and extracts user's current emotional state represented on the social network to recommend music, and evaluates the validity of our method through experiments.

• Proceedings of the IEEK Conference
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• 2003.11c
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• pp.289-292
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• 2003

이 논문은 WCDMA 와 안테나 배열 시스템에서 저 레벨 양자화와 MUSIC 알고리즘을 사용하여 신호의 방향을 추정하는 방법을 제안한다. 추가의 Power-Up Function 이 필요 없는 방향각 방법으로 이동가입자의 위치를 알아내기 위해 안테나 배열을 이용하여, WCDMA 시스템에서 역확산 코드로 다수의 신호를 분리하고, 각 신호를 저 레벨로 양자화 시켜 MUSIC 으로 신호의 방향각을 추정하였다. 이 방법을 이용하면 단말기의 안테나 출력파워가 낮더라도 기존 방법의 에러율과 비슷함을 시뮬레이션 결과로 알 수 있고, 양자화 비트를 처리하기 위해 필요한 메모리 또한 줄일 수 있어 하드웨어의 비용을 줄일 수 있을 것이다.

• Journal of Music and Human Behavior
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• v.10 no.2
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• pp.55-77
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• 2013

The purpose of this study was to identify current trends in music therapy literature in Korea in order to provide the groundwork for future research. Therefore, 122 articles from the Korean Journal of Music Therapy (KJMT) from 1999 to 2012 and 76 articles from the Journal of Music and Human Behavior (JMHB, formerly known as Korean Journal of Music Therapy Education) from 2004 to 2012 were analyzed. Analysis was performed by identifying the frequency and percentage of the following items: type, population, topic, and methodology of published articles. In addition, the results obtained were compared and contrasted between these two publications. Overall, it was found that there was a high prevalence of descriptive studies (KJMT, 46%,; JMHB, 45%), applied research (KJMT, 51%; JMHB 66%), and quantitative studies (KJMT, 82%; JMHB 37%). In addition, ordinary people with no particular diagnosis were the most often studied population (34%). Differences were found in that a historical study was only found in KJMT whereas a philosophical study was published only in JMHB. Further analysis revealed that JMHB included a higher proportion of applied research than KJMT. In KJMT, quantitative research was appeared twice as often as qualitative and mixed-method research combined. On the other hand, a similar number of each of the three methodological types of studies appeared in JMHB. In conclusion, this study indicates that more effort should be made to increase the quantity and improve the quality of professional publications in the field of music therapy in Korea.

• The Journal of the Acoustical Society of Korea
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• v.36 no.6
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• pp.436-441
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• 2017

Computing music similarity is indispensable in constructing music retrieval system. This paper focuses on the cover song search among various music-retrieval tasks. We investigate the cover song search method based on the chroma n-gram to reduce storage for feature DB and enhance search accuracy. Specifically we propose t-tab n-gram, n-gram selection method, and n-gram set comparison method. Experiments on the widely used music dataset confirmed that the proposed method improves cover song search accuracy as well as reduces feature storage.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.10
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• pp.1139-1144
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• 2014

Recently, the meditation is getting a major concern because of the trend of wellbeing. The various meditation methods are introduced, but, we need to take an appropriate method for himself or herself. Especially, the numbers of meditators who are using meditation music while they are meditating are increasing. Some people say it is helpful, but others don't. Therefore, I have studied the impact of the meditation music in this paper. I have compared between the meditation with the music and without the music by measuring the channels of left and right hemisphere of prefrontal lobe with 14-channel EEG device. For the result, I have found that there are great difference between two experiments.