• International Journal of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.27-32
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• 2024

Reliable and fine-grained musical metadata are required for efficient search of rapidly increasing music files. In particular, since the primary motive for listening to music is its emotional effect, diversion, and the memories it awakens, emotion classification along with genre classification of music is crucial. In this paper, as an initial approach towards a "ground-truth" dataset for music emotion and genre classification, we elaborately generated a music corpus through labeling of a large number of ordinary people. In order to verify the suitability of the dataset through the classification results, we extracted features according to MPEG-7 audio standard and applied different machine learning models based on statistics and deep neural network to automatically classify the dataset. By using standard hyperparameter setting, we reached an accuracy of 93% for genre classification and 80% for emotion classification, and believe that our dataset can be used as a meaningful comparative dataset in this research field.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.9
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• pp.4212-4217
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• 2013

Since genre of popular music began to classified, Alternative Music has been music form with most complex and diverse sub genre. This paper focused on mostly Alternative Rock, which began to attract attention since the 1990's, and analysed the cause for ambiguity of this genre. Therefore we can examine the inevitable limitation of traditional - based on musical form only - classification.

• Journal of the Korean Society of Costume
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• v.59 no.2
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• pp.154-168
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• 2009

This study aims on providing a design technique that expresses aesthetical elements by arranging the analysis of sensual beauty into detailed elements of design from music video outfits by genre of pop music, by observing music videos of female vocalists chosen from each genre of pop music focusing on their fashion. The results of this study are the following. 1. In the genre rock, the sensual beauty of female were expressed with a boyish and neutral style using texture such as leather or denim, and such style had the effect of emphasizing their feminine side even more. 2. In the genre dance music, exposure is extensive compared to other genre using sexy or lingerie look, and I found an ambivalent style of feminism with clothes in the form of drapery using textures such as chiffon and silk, and femme fatale style with textures adhering to the body such as leggings, leotard, and bodysuit. 3. In the genre of rap and hip-hop, clothes from casual and costume-play style were found using training jersey, t-shirt, and denim pants, and emphasized the sensual beauty of women by showing a silhouette with short length and fitting style using shiny textures. 4. In the genre of R&B, there were diverse outfits that suits the characteristics of characters appearing in the stories, or the situation of the story since there are many dramatic representation in the form of story Especially in case of female characters, the feminine side was emphasized staging a feminine style by wearing dresses with the texture of chiffon and silk. Exposure was restrained compared to other genre.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.07a
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• pp.714-716
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• 2020

We propose a unique client-driven music genre classification solution, that can identify the music genre using a deep convolutional neural network operating on the time-domain signal. The proposed method uses the client device (Jetson TX2) computational resources to identify the music genre. We use the industry famous GTZAN genre collection dataset to get reliable benchmarking performance. HTTP live streaming (HLS) client and server sides are designed locally to validate the effectiveness of the proposed method. HTTP persistent broadcast connection is adapted to reduce corresponding responses and network bandwidth. The proposed model can identify the genre of music files with 97% accuracy. Due to simplicity and it can support a wide range of client hardware.

• 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.

• Journal of Korea Game Society
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• v.20 no.2
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• pp.75-90
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• 2020

The purpose of this study is to identify the functional role of music, which is one of the main elements that make up PC games. To this end, theme music by genre of PC game was divided through analysis and then the relationship between variables was analyzed by visually positioning the distance between 'music type', 'structural element of music' and 'image' using multiple correspondence matching analysis. As a result, it was confirmed that there was a difference in location between the types of music by game genre, the structural elements of music, and the characteristics of the image.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.4
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• pp.1869-1886
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• 2018

This paper proposes a MIDI- and audio-based music genre classification method for Korean traditional music. There are many traditional instruments in Korea, and most of the traditional songs played using the instruments have similar patterns and rhythms. Although music information processing such as music genre classification and audio melody extraction have been studied, most studies have focused on pop, jazz, rock, and other universal genres. There are few studies on Korean traditional music because of the lack of datasets. This paper analyzes raw audio and MIDI phrases in Korean traditional music, performed using Korean traditional musical instruments. The classified samples and MIDI, based on our classification system, will be used to construct a database or to implement our Kontakt-based instrument library. Thus, we can construct a management system for a Korean traditional music library using this classification system. Appropriate feature sets for raw audio and MIDI phrases are proposed and the classification results-based on machine learning algorithms such as support vector machine, multi-layer perception, decision tree, and random forest-are outlined in this paper.

• Journal of Korea Multimedia Society
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• v.10 no.7
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• pp.817-826
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• 2007

The existing a music genre classification research used signal feature of the part which gets 20 seconds interval of the random or the $40%{\sim}45%$ after in the music. This paper propose it to increase the accuracy of existing research to classify music genre using climax part in the music. Generally the music is divided to three parts; introduction, progress and climax. And the climax is the part which the music emphasizes and expresses the feature of the music best. So, we can get efficient result if the climax is used, when the music classify. We can get the climax in the music finding the tempo and node which uses FFT and the maximum waveform from each node. In this paper, we did a genre classification experiment which uses existing research method and proposing method. The existing method expressed 47% accuracy. And proposing method expressed 56% accuracy which is improved than existing method.

• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.7
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• pp.842-848
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• 2020

Music markets have entered the era of streaming. In order to select and propose music that suits the taste of music consumers, there is an active demand and research on an automatic music genre classification system. We propose a method to improve the accuracy of genre unclassified songs, which was a lack of the previous system, by using a generative adversarial network (GAN) to further develop the automatic voting system for deep learning music genre using Softmax proposed in the previous paper. In the previous study, if the spectrogram of the song was ambiguous to grasp the genre of the song, it was forced to leave it as an unclassified song. In this paper, we proposed a system that increases the accuracy of genre classification of unclassified songs by converting the spectrogram of unclassified songs into an easy-to-read spectrogram using GAN. And the result of the experiment was able to derive an excellent result compared to the existing method.