• International journal of advanced smart convergence
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• v.9 no.3
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• pp.239-245
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• 2020

The Ewha Music Database (EMDB) is an online database comprised of primary source materials related to music education from East Asia during the modern era (1880 to 1945) when Korea, Japan, and China were geopolitically and culturally intertwined. We developed the incipit search in EMDB as an embedded tool. This is the first attempt in Korea to implement a unique search function of musical data using alphabets of musical notes. Unlike in traditional search system that uses general literature information search conditions, such as author, title, publisher, year, number of pages, etc., it offers a new way of searching a musical piece/work and sheet music. This study confirms that digital information technology is an important methodology for research of music culture as a field of humanities.

• The Journal of the Acoustical Society of Korea
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• v.26 no.3E
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• pp.69-73
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• 2007

This paper presents an efficient algorithm to retrieve similar music files from a large archive of digital music database. Users are able to navigate and discover new music files which sound similar to a given query music file by searching for the archive. Since most of the methods for finding similar music files from a large database requires on computing the distance between a given query music file and every music file in the database, they are very time-consuming procedures. By measuring the acoustic distance between the pre-classified music files with the same type of emotion, the proposed method significantly speeds up the search process and increases the precision in comparison with the brute-force method.

• Journal of Convergence for Information Technology
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• v.11 no.9
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• pp.214-220
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• 2021

With the rapid spread of smartphones, it has become common to listen to music everywhere, just like background music in life, so it is necessary to create a music database that can make recommendations according to individual circumstances and conditions. This paper proposes a music recommendation model through social media. Since emotions, situations, time of day, weather, etc. are included in hashtags, it is possible to build a social media-based database that reflects the opinions of various people with collective intelligence. We use web crawling to collect and categorize different hashtags from posts with music title hashtags to use real listeners' opinions about music in a database. Data from social media is used to create a music database, and music is classified in a different way from collaborative filtering, which is mainly used by existing music platforms.

• The Journal of Korean Association of Computer Education
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• v.8 no.5
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• pp.109-118
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• 2005

In this paper, we present a music retrieval system that compares the geometric structure of a melody specified by a user with those in a music database. The system finds matches between a query melody and melodies in the database by analyzing both structural and contextual features. The retrieval method is based on the geometric hashing algorithm which consists of two steps; the preprocessing step and the recognition step. During the preprocessing step, we divide a melody into several fragments and analyze the pitch and duration of each note of the fragments to find a structural feature. To find a contextual feature, we find a main chord for each fragment. During the recognition step, we divide the query melody specified by a user into several fragments and search through all fragments in the database that are structurally and contextually similar to the melody. A vote is cast for each of the fragments and the music whose total votes are the maximum is the music that contains a matching melody against the query melody. Using our approach, we can find similar melodies in a music database quickly. We can also apply the method to detect plagiarism in music.

• Proceedings of the Korea Multimedia Society Conference
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• 2012.05a
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• pp.300-300
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• 2012

A database is an organized collection of data, today typically in digital form. The data are typically organized to model relevant aspects of reality, in a way that supports processes requiring this information. A good database is designed for a specific use and is constructed with the possibility of growth. In this project, we collect music education data of the East Asia and try to build a database that can share the primary data based on this collection. Hence we can provide opportunity to study about Korea modern music and culture in a broader perspective. In this paper, we explore the database construction methodology for implementing on this project and we see over about data entry and management.

• The KIPS Transactions:PartA
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• v.10A no.6
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• pp.737-748
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• 2003

With the popularity of digital music contents, querying and retrieving music contents efficiently from database has become essential. In this paper, we propose a Fast Melody Finder (FMF) that can retrieve melodies fast and efficiently from music database using frequently queried tunes. This scheme is based on the observation that users have a tendency to memorize and query a small number of melody segments, and indexing such segments enables fast retrieval. To handle those tunes, FMF transcribes all the acoustic and common music notational inputs into a specific string such as UDR and LSR. We have implemented a prototype system and showed on its performance through various experiments.

• 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 Korea Multimedia Society
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• v.16 no.4
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• pp.453-463
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• 2013

In this paper, we present an effective method and a system for the music summarization which automatically extract the chorus portion of a piece of music. A music summary technology is very useful for browsing a song or generating a sample music for an online music service. To develop the solution, conventional automatic music summarization methods use a 2-dimensional similarity matrix, statistical models, or clustering techniques. But our proposed method extracts the music summary by calculating BER(Bit Error Rate) between audio fingerprint blocks which are extracted from a song. But we could directly use an enormous audio fingerprint database which was already saved for a music retrieval solution. This shows the possibility of developing a various of new algorithms and solutions using the audio fingerprint database. In addition, experiments show that the proposed method captures the chorus of a song more effectively than a conventional method.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.419-420
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• 2007

In this method, we extract peak periods using energy contents of each segment of music. This feature extraction method is equally applied on both the training and query music. Similarity matching algorithm is applied on the extracted feature values for identifying the query music from the database. The retrieval accuracy of 95% of our method is a pretty good result.

• The Journal of the Acoustical Society of Korea
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• v.30 no.2
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• pp.100-106
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• 2011

Music recordings have been digitalized such that huge size of music database is available to the public. Thus, the automatic classification system of music genres is required to effectively manage the growing music database. Mel-Frequency Cepstral Coefficient (MFCC) is a popular feature vector for genre classification. In this paper, the combined super-vector with Decorrelated Filter Bank (DFB) and Octave-based Spectral Contrast (OSC) using texture windows is processed by Support Vector Machine (SVM) for genre classification. Even with the lower order of the feature vector, the proposed super-vector produces 4.2 % improved classification accuracy compared with the conventional Marsyas system.