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RISS 인기검색어
- 검색키워드
- 저자 : Babu Kaji Baniya (검색결과 5 건)
- 무료
- 기관 내 무료
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Rough Set-Based Approach for Automatic Emotion Classification of Music
( Babu Kaji Baniya ),( Joonwhoan Lee ) 한국정보처리학회 2017 Journal of information processing systems Vol.13 No.2
Music emotion is an important component in the field of music information retrieval and computational musicology. This paper proposes an approach for automatic emotion classification, based on rough set (RS) theory. In the proposed approach, four different sets of music features are extracted, representing dynamics, rhythm, spectral, and harmony. From the features, five different statistical parameters are considered as attributes, including up to the 4<sup>th</sup> order central moments of each feature, and covariance components of mutual ones. The large number of attributes is controlled by RS-based approach, in which superfluous features are removed, to obtain indispensable ones. In addition, RS-based approach makes it possible to visualize which attributes play a significant role in the generated rules, and also determine the strength of each rule for classification. The experiments have been performed to find out which audio features and which of the different statistical parameters derived from them are important for emotion classification. Also, the resulting indispensable attributes and the usefulness of covariance components have been discussed. The overall classification accuracy with all statistical parameters has recorded comparatively better than currently existing methods on a pair of datasets.
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Rough Set-Based Approach for Automatic Emotion Classification of Music
Baniya, Babu Kaji,Lee, Joonwhoan Korea Information Processing Society 2017 Journal of information processing systems Vol.13 No.2
Music emotion is an important component in the field of music information retrieval and computational musicology. This paper proposes an approach for automatic emotion classification, based on rough set (RS) theory. In the proposed approach, four different sets of music features are extracted, representing dynamics, rhythm, spectral, and harmony. From the features, five different statistical parameters are considered as attributes, including up to the $4^{th}$ order central moments of each feature, and covariance components of mutual ones. The large number of attributes is controlled by RS-based approach, in which superfluous features are removed, to obtain indispensable ones. In addition, RS-based approach makes it possible to visualize which attributes play a significant role in the generated rules, and also determine the strength of each rule for classification. The experiments have been performed to find out which audio features and which of the different statistical parameters derived from them are important for emotion classification. Also, the resulting indispensable attributes and the usefulness of covariance components have been discussed. The overall classification accuracy with all statistical parameters has recorded comparatively better than currently existing methods on a pair of datasets.
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Music Genre Classification Based on Timbral Texture and Rhythmic Content Features
( Babu Kaji Baniya ),( Deepak Ghimire ),( Joonwhon Lee ) 한국정보처리학회 2013 한국정보처리학회 학술대회논문집 Vol.20 No.1
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.
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Label Prediction of the Unlabeled Mood of a Music Genre Using Semi-Supervised Learning
Babu Kaji Baniya,이준환 차세대컨버전스정보서비스학회 2015 차세대컨버전스정보서비스기술논문지 Vol.4 No.2
Music genre and mood classifications are vital components of the field of multimedia retrieval and computational musicology. There is a growing interest in their development to address the difficulties of music categorization. The proposed method finds the unlabeled mood of a music genre with the help of labeled music mood datasets using different audio feature sets. Semi-supervised learning, which exploits huge amounts of unlabeled data, together with the limited labeled data for learning, has attracted a great deal of research interests. In this paper, we propose diverse audio features to precisely characterize music content. The feature sets belong to four groups: dynamic, rhythmic, spectral, and harmonic. A bin histogram was calculated from each feature to preserve all the important information associated with it. From the extracted audio features, we first tried to find the unlabeled mood of a music genre by using the labeled mood dataset. Harmonic and consistency (local and global) semi-supervised learning algorithms were considered to determine the unknown mood label of a music genre. In the next stage, we also evaluated whether unlabeled genre datasets would influence the mood classification accuracy. The unlabeled datasets were added to the training set in different proportions, so that the overall impact on classification accuracy could be analyzed. We improved the classification accuracy using an unlabeled music genre dataset in training. In the last section, we verified the classification accuracy by adding an unlabeled genre dataset to label mood with only the mood dataset (without adding a genre dataset).
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