• Journal of Multimedia Information System
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• 제4권3호
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• pp.99-106
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• 2017

This study does a qualitative and quantitative analysis of how music by humming is being used by music educators in the classroom. Music by humming is part division of music information retrieval. In order to define what a music information retrieval system is first I need to define what it is. Berger and Lafferty (1999) define information retrieval as "someone doing a query to a retrieval system, a user begins with an information need. This need is an ideal document- perfect fit for the user, but almost certainly not present in the retrieval system's collection of documents. From this ideal document, the user selects a group of identifying terms. In the context of traditional IR, one could view this group of terms as akin to expanded query." Music Information Retrieval has its background in information systems, data mining, intelligent systems, library science, music history and music theory. Three rounds of surveys using question pro where completed. The study found that there were variances in knowledge, training and level of awareness of query by humming, music information retrieval systems. Those variance relationships where based on music specialty, level that they teach, and age of the respondents.

• 한국HCI학회:학술대회논문집
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• 한국HCI학회 2006년도 학술대회 1부
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• pp.400-405
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• 2006

In this paper, we present effective methods for music summarization which summarize music automatically. It could be used for sample music of on-line digital music provider or some music retrieval technology. When summarizing music, we use different two methods according to music length. First method is for finding sabi or chorus part of music which can be regarded as the most important part of music and the second method is for extracting several parts which are in different structure or have different mood in the music. Our proposed music summarization system is better than conventional system when structure of target music is explicit. The proposed method could generate just one important segment of music or several segments which have different mood in the music. Thus, this scheme will be effective for summarizing music in several applications such as online music streaming service and sample music for Tcommerce.

• 한국HCI학회:학술대회논문집
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• 한국HCI학회 2007년도 학술대회 1부
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• pp.268-273
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• 2007

In this paper, we present effective methods for automatic music recommendation system which automatically recommend music by signal processing technology. Conventional music recommendation system use users’ music downloading pattern, but the method does not consider acoustic characteristics of music. Sometimes, similarities between music are used to find similar music for recommendation in some method. However, the feature used for calculating similarities is not highly related to music characteristics at the system. Thus, our proposed method use high-level music characteristics such as rhythm pattern, timbre characteristics, and the lyrics. In addition, our proposed method store features of music, which individuals queried, to recommend music based on individual taste. Experiments show the proposed method find similar music more effectively than a conventional method. The experimental results also show that the proposed method could be used for real-time application since the processing time for calculating similarities between music, and recommending music are fast enough to be applicable for commercial purpose.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제8권3호
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• pp.192-195
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• 2008

People have tendency of forgetting music title, though they easily remember particular part of music. If a music search system can find the title through a part of melody, this will provide very convenient interface to users. In this paper, we propose an algorithm that enables this type of search using feature transformation function. The original music is transformed to new feature information with sequential melodies. When a melody that is a part of search music is given to the system, the music retrieval system searches the music similar to the feature information of the melody. Moreover, this transformation function can be easily extended to various music recognition systems.

• 한국컴퓨터정보학회논문지
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• 제21권4호
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• pp.39-46
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• 2016

While the difficulty of the music can be classified by a variety of standard, conventional methods are classified by the subjective judgment based on the experience of many musicians or conductors. Music score is difficult to evaluate as there is no quantitative criterion to determine the degree of difficulty. In this paper, we propose a new classification method for determining the degree of difficulty of the music. In order to determine the degree of difficulty, we convert the score, which is expressed as a traditional music score, into electronic music sheet. Moreover, we calculate information about the elements needed to play sheet music by distance of notes, tempo, and quantifying the ease of interpretation. Calculating a degree of difficulty of the entire music via the numerical data, we suggest the difficulty evaluation of the score, and show the difficulty of music through experiments.

• Journal of Information Processing Systems
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• 제14권1호
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• pp.32-55
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• 2018

The digitization of music has seen a considerable increase in audience size from a few localized listeners to a wider range of global listeners. At the same time, the digitization brings the challenge of smoothly retrieving music from large databases. To deal with this challenge, many systems which support the smooth retrieval of musical data have been developed. At the computational level, a query music piece is compared with the rest of the music pieces in the database. These systems, music information retrieval (MIR systems), work for various applications such as general music retrieval, plagiarism detection, music recommendation, and musicology. This paper mainly addresses two parts of the MIR research area. First, it presents a general overview of MIR, which will examine the history of MIR, the functionality of MIR, application areas of MIR, and the components of MIR. Second, we will investigate music similarity measurement methods, where we provide a comparative analysis of state of the art methods. The scope of this paper focuses on comparative analysis of the accuracy and efficiency of a few key MIR systems. These analyses help in understanding the current and future challenges associated with the field of MIR systems and music similarity measures.

• 한국문헌정보학회지
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• 제26권
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• pp.75-112
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• 1994

The purpose of this study is to investigate the problems concerning 780 music division of DDC. The object is especially arrangement of 780 music in the 20th edition of DDC which is the complete revision. The result is summarized as follows : 1. Although music is an important subject in humanities, especially in arts, it was classified as one division (780) not class. 2. The arrangement of 780 music is severely west-oriented music theory, vocal music and instrumental music. 3. Classification number of 780 music becomes longer because of the limitation of decimal notation. 4. 780 music division of DDC neglects music theory and emphasizes music practicing, especially performance. 5. The assignment of classification number is unbalanced, especially between theory and practice, composition and performance, and among sub-sections of vocal and instrumental music. 6. Many important subject are omitted in DDC music schedule, for example, musicology and branches of musicology, composition and traditional instruments of many countries. 7. Employment of terminology is often improper and inconsistant.

• The Journal of the Acoustical Society of Korea
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• 제22권4E호
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• pp.141-149
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• 2003

As music of digital form has been widely used, many people have been interested in the automatic extraction of natural information of music itself, such as key of a music, chord progression, melody progression, tempo, etc. Although some studies have been tried, consistent and reliable results of musical information extraction had not been achieved. In this paper, we propose a method to extract chord and tempo information from general polyphonic music signals. Chord can be expressed by combination of some musical notes and those notes also consist of some frequency components individually. Thus, it is necessary to analyze the frequency components included in musical signal for the extraction of chord information. In this study, we utilize a sinusoidal modeling, which uses sinusoids corresponding to frequencies of musical tones, and show reliable chord extraction results of sinusoidal modeling. We could also find that the tempo of music, which is the one of remarkable feature of music signal, interactively supports the chord extraction idea, if used together. The proposed scheme of musical feature extraction is able to be used in many application fields, such as digital music services using queries of musical features, the operation of music database, and music players mounting chord displaying function, etc.

• 한국컴퓨터정보학회논문지
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• 제15권11호
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• pp.91-99
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• 2010

최근 디지털 콘텐츠 서비스 분야에서 사용자 맞춤형 서비스를 위해 사용자 자원 인식의 필요성이 대두되고 있다. 특히 온라인 기반 음악 서비스의 경우 사용자 취향 분석, 음원 추천 및 음악 관련 정보 제공을 위해 사용자 음원인식 기술이 요구되고 있다. 현재 태그정보를 기초로 사용자 음원 인식 후 음악 관련 정보를 제공하는 서비스가 제공되고 있지만, 태그정보의 변조 및 삭제 등의 취약점으로 인식 오류가 급증하고 있다. 이러한 문제의 보완 방안으로 음악 자체를 이용하는 내용기반 사용자 음원 인식 기법에 대한 연구가 이루어지고 있다. 본 논문에서는 음악의 파형에서 추출된 특징 정보를 기초로 온라인상에서 사용자 음원을 인식하는 방법에 대해 논하고자 한다. 사용자 음원의 내용기반 인식을 위해 구조에 적합한 음원의 전처리 후 특징 추출을 하였다. 추출된 특징은 음악 서버에 특징 형태로 저장된 음원과의 매칭 과정을 통한 인식을 진행하여 태그데이터에 독립적으로 사용자 음원을 인식할 수 있게 되었다. 제안된 사용자 음원 인식 방법의 검증을 위해 600개의 음악을 무작위 선정하고, 각각을 5가지 음질로 변화하였다. 이렇게 생성된 3000개의 실험음원을 30만곡을 포함하는 음악 서버를 기준으로 인식실험을 진행하였다. 평균 인식율은 85%를 나타내었다. 제안하는 내용기반 음원 인식을 통하여 태그기반 음원 인식의 취약점에 대한 극복을 하였으며, 음원 인식의 성능은 실제 온라인 음악 서비스에 적용할 가능성을 보여주었다.

• International Journal of Contents
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• 제8권2호
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• pp.1-6
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• 2012

Recently, content-based music information retrieval (MIR) systems for mobile devices have attracted great interest. However, music retrieval systems are greatly affected by background noise when music is recorded in noisy environments. Therefore, we evaluated various pre-processing methods using the Philips method to determine the one that performs most robust music retrieval in such environments. We found that dynamic noise reduction (DNR) is the best pre-processing method for a music retrieval system in noisy environments.