• Journal of the Korean BIBLIA Society for library and Information Science
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• v.31 no.1
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• pp.115-136
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• 2020

It is necessary to find a genre classification by reflecting the needs of users since a subject that makes up the highest proportion of books in the school library is fictions in literature and KDC cannot accept user's need to access fiction in school libraries. This study suggested the genre classification for fictions in school libraries through surveying classification of fictions in domestic and foreign libraries, and comparing between classification systems of online/offline bookstores, KDC and DDC. For developing the genre classification system, it is to collect genre terms for fictions, to extract 14 genre headings among them, and to assign the acronym of English genre terms as classification notation. For applying the newly developed genre classification, KDC number of one middle school library was converted as the 3 methods such as combination of KDC, genre term before 800 and only genre terms. This study could contribute to suggest the genre classification of fiction to reflect user needs and to overcome the limitation of hierachical classification in KDC.

• Journal of KIISE:Software and Applications
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• v.30 no.11
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• pp.1062-1071
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• 2003

A genre-based classification means classifying documents by the purpose for which they were written, not by the semantics or subject areas. Most genre classifying methods in the past were based on the existing documents categorization algorithms and ineffective for feature selections, resulting in low quality classification results. In this research, we propose a new method for automatic classification of digital documents by genre. The genre classifier we developed uses the deviation statistic between the genre-revealing term frequencies and between the subject-revealing term frequencies within a genre. We collected Web documents to evaluate the proposed genre classification method. The experimental results show that the proposed method outperforms a direct application of a kai-square feature selection and bayesian classifier often used for subject classification by proving an excellent accuracy of about 30 percent.

• 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.

• The Journal of the Acoustical Society of Korea
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• v.29 no.4
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• pp.265-269
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• 2010

This paper proposes a novel spectral audio feature, spectral contrast MFCC (SCMFCC), and studies its performance on the musical genre classification. For a successful musical genre classifier, extracting features that allow direct access to the relevant genre-specific information is crucial. In this regard, the features based on the spectral contrast, which represents the relative distribution of the harmonic and non-harmonic components, have received increased attention. The proposed SCMFCC feature utilizes the spectral contrst on the mel-frequency cepstrum and thus conforms the conventional MFCC in a way more relevant for musical genre classification. By performing classification test on the widely used music DB, we compare the performance of the proposed feature with that of the previous ones.

• Journal of Korean Library and Information Science Society
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• v.41 no.4
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• pp.151-170
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• 2010

There is a growing interest in user-centered classification or reader-interest classification, as questions have arisen from the meanings and the effects of traditional library classification. American public libraries have used fiction genre classification called bookstore model as an alternative to the traditional classification schemes. As a result, accessibility to the collection was promoted and library service for their users was improved. This study intends to make a comprehensive inquiry about the philosophical background and functional features of genre classification. To the end, literature survey and interviews or e-mails with librarians in American public libraries were conducted.

• The Journal of the Acoustical Society of Korea
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• v.33 no.1
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• pp.81-86
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• 2014

This paper presents a study on the effectiveness of using the spectral and the temporal octave-band order statistics for musical genre classification. In order to represent the relative disposition of the harmonic and non-harmonic components, we utilize the octave-band order statistics of power spectral distribution. Experiments on the widely used two music datasets were performed; the results show that the octave-band order statistics improve genre classification accuracy by 2.61 % for one dataset and 8.9 % for another dataset compared with the mel-frequency cepstral coefficients and the octave-band spectral contrast. Experimental results show that the octave-band order statistics are promising for musical genre classification.

• Proceedings of the IEEK Conference
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• 2004.08c
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• pp.768-771
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• 2004

In this paper, we propose a content-based audio genre classification algorithm that automatically classifies the query audio into five genres such as Classic, Hiphop, Jazz, Rock, Speech using digital signal processing approach. From the 20 second query audio file, 54 dimensional feature vectors, including Spectral Centroid, Rolloff, Flux, LPC, MFCC, is extracted from each query audio. For the classification algorithm, k-NN, Gaussian, GMM classifier is used. In order to choose optimum features from the 54 dimension feature vectors, SFS (Sequential Forward Selection) method is applied to draw 10 dimension optimum features and these are used for the genre classification algorithm. From the experimental result, we verify the superior performance of the SFS method that provides near $90{\%}$ success rate for the genre classification which means $10{\%}$-$20{\%}$ improvements over the previous methods

• The Journal of the Acoustical Society of Korea
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• v.36 no.2
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• pp.144-150
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• 2017

Musical genre is indispensible in constructing music information retrieval system, such as music search and classification. In general, the spectral characteristics of a music signal are obtained based on a subband decomposition to represent the relative distribution of the harmonic and the non-harmonic components. In this paper, we investigate the subband decomposition parameters in extracting features, which improves musical genre classification accuracy. In addition, the linear projection methods are studied to reduce the resulting feature dimension. Experiments on the widely used music datasets confirmed that the subband decomposition finer than the widely-adopted octave scale is conducive in improving genre-classification accuracy and showed that the feature-dimension reduction is effective reducing a classifier's computational complexity.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.8 no.4
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• pp.33-38
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• 2009

This paper proposes an automatic equalizer control method in automobile audio system. The proposed method discriminates the music segment from the consecutive real-time audio stream of the radio and the equalizer is controlled automatically according to the classified genre of the music segment. For enhancing the accuracy of the music genre classification in real-time, timbre feature and rhythm feature extracted from the consecutive audio stream is applied to GMM(Gaussian mixture model) classifier. The proposed method evaluates the performance of the music genre classification, which classified various audio segments segmented from the audio signal of the radio broadcast in automobile audio system into one of five music genres.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.10C
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• pp.965-974
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• 2007

In this paper, we proposed the feature selection algorithm for multi-class genre classification. In our proposed algorithm, we developed GMM separation score based on Gaussian mixture model for measuring separability between two genres. Additionally, we improved feature subset selection algorithm based on sequential forward selection for multi-class genre classification. Instead of setting criterion as entire genre separability measures, we set criterion as worst genre separability measure for each sequential selection step. In order to assess the performance proposed algorithm, we extracted various features which represent characteristics such as timbre, rhythm, pitch and so on. Then, we investigate classification performance by GMM classifier and k-NN classifier for selected features using conventional algorithm and proposed algorithm. Proposed algorithm showed improved performance in classification accuracy up to 10 percent for classification experiments of low dimension feature vector especially.