• Journal of Korea Multimedia Society
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• v.18 no.1
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• pp.52-64
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• 2015

Recently, as the quality of life has been improving, search items in the area of entertainment represent an increasing share of the total usage of Internet portal sites. Information retrieval in the entertainment area is mainly depending on keywords that users are inputting, and the results of information retrieval are the contents that contain those keywords. In this paper, we propose a search method that takes natural language inputs and retrieves the database pertaining to entertainment. The main components of our study are the simple Korean morphological analyzer using case particle information, predicate-oriented token generation, standardized pattern generation coherent to tokens, and automatic generation of the corresponding SQL queries. We also propose an efficient retrieval system that searches the most relevant results from the database in terms of natural language querying, especially in the restricted domain of music, and shows the effectiveness of our system.

• Journal of Internet Computing and Services
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• v.23 no.2
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• pp.45-52
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• 2022

With the development of artificial intelligence analysis methods, especially machine learning, various fields are widely expanding their application ranges. However, in the case of classical music, there still remain some difficulties in applying machine learning techniques. Genre classification or music recommendation systems generated by deep learning algorithms are actively used in general music, but not in classical music. In this paper, we attempted to classify opera among classical music. To this end, an experiment was conducted to determine which criteria are most suitable among, composer, period of composition, and emotional atmosphere, which are the basic features of music. To generate emotional labels, we adopted zero-shot classification with four basic emotions, 'happiness', 'sadness', 'anger', and 'fear.' After embedding the opera libretto with the doc2vec processing model, the optimal number of clusters is computed based on the result of the elbow method. Decided four centroids are then adopted in k-means clustering to classify unsupervised libretto datasets. We were able to get optimized clustering based on the result of adjusted rand index scores. With these results, we compared them with notated variables of music. As a result, it was confirmed that the four clusterings calculated by machine after training were most similar to the grouping result by period. Additionally, we were able to verify that the emotional similarity between composer and period did not appear significantly. At the end of the study, by knowing the period is the right criteria, we hope that it makes easier for music listeners to find music that suits their tastes.

• Proceedings of the Korean Information Science Society Conference
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• 2001.04b
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• pp.148-150
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• 2001

MusicXML, ScoreML과 같은 포맷은 음악을 XML로 표현한 것으로 음정, 박자, 조성, 리듬, 화음 등 모든 정보를 표현하기 위해 고안되었다. 즉, XML이 가지고 있는 간단성, 확장성, 재사용성의 장점을 가지며 분석, 검색, 표기법에 훨씬 큰 장점을 가진 마크업 언어들이다. 기존의 음악 검색에 대한 연구들이 음악파일에 대한 것에 행해졌던 것에 비해 본 논문은 음악을 덱스트로 표현한 XML에서 효과적인 선율 검색을 위해 계이름을 사용한 검색시스템을 제안한다.

• Journal of KIISE:Databases
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• v.34 no.4
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• pp.283-303
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• 2007

Recently, the study of efficient way to store and retrieve enormous music data is becoming the one of important issues in the multimedia database. Most general method of MIR (Music Information Retrieval) includes a text-based approach using text information to search a desired music. However, if users did not remember the keyword about the music, it can not give them correct answers. Moreover, since these types of systems are implemented only for exact matching between the query and music data, it can not mine any information on similar music data. Thus, these systems are inappropriate to achieve similarity matching of music data. In order to solve the problem, we propose an Efficient Query-By-Humming System (EQBHS) with a content-based indexing method that efficiently retrieve and store music when a user inquires with his incorrect humming. For the purpose of accelerating query processing in EQBHS, we design indices for significant melodies, which are 1) frequent melodies occurring many times in a single music, on the assumption that users are to hum what they can easily remember and 2) melodies partitioned by rests. In addition, we propose an error tolerated mapping method from a note to a character to make searching efficient, and the frequent melody extraction algorithm. We verified the assumption for frequent melodies by making up questions and compared the performance of the proposed EQBHS with N-gram by executing various experiments with a number of music data.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.05a
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• pp.204-207
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• 2013

Music genre classification is an essential component for music information retrieval system. There are two important components to be considered for better genre classification, which are audio feature extraction and classifier. This paper incorporates two different kinds of features for genre classification, timbral texture and rhythmic content features. Timbral texture contains several spectral and Mel-frequency Cepstral Coefficient (MFCC) features. Before choosing a timbral feature we explore which feature contributes less significant role on genre discrimination. This facilitates the reduction of feature dimension. For the timbral features up to the 4-th order central moments and the covariance components of mutual features are considered to improve the overall classification result. For the rhythmic content the features extracted from beat histogram are selected. In the paper Extreme Learning Machine (ELM) with bagging is used as classifier for classifying the genres. Based on the proposed feature sets and classifier, experiment is performed with well-known datasets: GTZAN databases with ten different music genres, respectively. The proposed method acquires the better classification accuracy than the existing approaches.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10c
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• pp.183-188
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• 2006

허밍을 통한 유사 검색 질의가 주어질 때 효과적으로 음악 데이터베이스를 검색하는 시스템에 대한 연구는 다양한 방향으로 진행되어 왔다. 최근에는 음악 데이터와 허밍 질의를 시계열 데이터로 보고 시계열 데이터 유사 검색과 관련하여 제안되어 왔던 여러 가지 거리 척도(distance measure)나 인덱싱 기법등을 적용하여 효과적으로 질의를 처리하려는 시도가 계속 되고 있다. 허밍 질의의 특성을 고려한 균일 스케일링(Uniform Scaling)을 사용하여 효과적인 유사 검색을 하는 방법은 가장 최근 제시된 방법 중 하나이다. 본 논문에서는 허밍을 통한 유사 검색 시스템인 Humming BIRD(Humming Based similaR miDi music retrieval system)를 제안하고 구현하였다. 슬라이딩 윈도우를 사용하여 음악의 임의의 부분에 대한 허밍 질의를 처리할 수 있도록 하였으며 효율적인 검색을 위해 중심을 일치시킨(center-aligned) 균일 스케일링을 제안하고 이 거리의 하한을 계산하는 하계 함수를 사용하여 탐색 공간(search space)을 효과적으로 줄여 더 빠르고 효과적인 유사 검색을 가능하도록 하였으며 실험을 통해 중심을 일치시킨된 균일 스케일링이 이전과 같은 검색 결과를 얻으면서도 효과적으로 검색함을 탐색 공간을 줄이는 가지치기 성능을 비교함으로써 보였다.

• International Journal of Contents
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• v.11 no.4
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• pp.1-6
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• 2015

In practice, the most important performance parameters for music information retrieval (MIR) service are robustness of fingerprint in real noise environments and recognition accuracy when the obtained query clips are matched with the an entry in the database. To satisfy these conditions, we proposed a conjoined fingerprint algorithm for use in massive MIR service. The conjoined fingerprint scheme uses interhash and intrahash algorithms to produce a robust fingerprint scheme in real noise environments. Because the interhash and intrahash algorithms are masked in the predominant pitch estimation, a compact fingerprint can be produced through their relationship. Experimental performance comparison results showed that our algorithms were superior to existing algorithms, i.e., the sub-mask and Philips algorithms, in real noise environments.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.2
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• pp.124-128
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• 2011

Music emotion recognition is currently one of the most attractive research areas in music information retrieval. In order to use emotion as clues when searching for a particular music, several music based emotion recognizing systems are fundamentally utilized. In order to maximize user satisfaction, the recognition accuracy is very important. In this paper, we develop a new music emotion recognition system, which employs a multilabel feature selector and multilabel classifier. The performance of the proposed system is demonstrated using novel musical emotion data.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.725-732
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• 2009

Thanks to the development of the Internet, people can easily access a vast amount of music. This brings attention to application systems such as a melody-based music search service or music recommendation service. Extracting melodies from music is a crucial process to provide such services. This paper introduces a novel algorithm that can extract melodies from piano music. Since piano can produce polyphonic music, we expect that by studying melody extraction from piano music, we can help extract melodies from general polyphonic music.

• Journal of KIISE:Databases
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• v.34 no.5
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• pp.409-419
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• 2007

Database community focuses on the similar music retrieval systems for music database when a humming query is given. One of the approaches is converting the midi data to time series, building their indices and performing the similarity search on them. Queries based on humming can be transformed to time series by using the known pitch detection algorithms. The recently suggested algorithm, scaled and warped matching, is based on dynamic time warping and uniform scaling. This paper proposes Humming BIRD(Humming Based sImilaR mini music retrieval system) using sliding window and center-aligned scaled and warped matching. Center-aligned scaled and warped matching is a mixed distance measure of center-aligned uniform scaling and time warping. The newly proposed measure gives tighter lower bound than previous ones which results in reduced search space. The empirical results show the superiority of this algorithm comparing the pruning power while it returns the same results.