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

• Journal of IKEEE
• /
• v.13 no.2
• /
• pp.167-174
• /
• 2009

Music evokes human emotions or creates music moods through various low-level musical features. Typical music clip consists of one or more moods and this can be used as an important criteria for determining the similarity between music clips. In this paper, we propose a new music retrieval scheme based on the mood change patterns of music clips. For this, we first divide music clips into segments based on low level musical features. Then, we apply K-means clustering algorithm for grouping them into clusters with similar features. By assigning a unique mood symbol for each cluster, we can represent each music clip by a sequence of mood symbols. Finally, to estimate the similarity of music clips, we measure the similarity of their musical mood sequence using the Longest Common Subsequence (LCS) algorithm. To evaluate the performance of our scheme, we carried out various experiments and measured the user evaluation. We report some of the results.

• Journal of the Korea Society of Computer and Information
• /
• v.26 no.2
• /
• pp.45-52
• /
• 2021

In this paper, we propose a method of generating theme, background amd signal music usage monitoring information based on a blockchain, in which the music usage informations are recorded by the monitoring tool using feature-based filtering of monitoring organizations. Theme, background and signal music are music inserted into the broadcasting contents of broadcaster. Since they are recognized as created contents just like normal music, there are lyricists and composers who have the right for those music and all copyright holders of them have to receive the corresponding copyright fees, once the music was used in the broadcast. However, there are problems with inaccurate monitoring results for music usage, due to the omission of usage details and non-transparent settlement method. In order to solve these problems, If the information generation method proposed in this paper, accurate music usage history can be created, the details are stored in the blockchain without changes or omissions, and transparent settlement and distribution are possible by smart contract, avoiding the current non-transparent settlement method.

• Journal of Korea Multimedia Society
• /
• v.21 no.7
• /
• pp.737-744
• /
• 2018

This paper presents a study on the performance of the higher-order moments for speech/music classification. For a successful speech/music classifier, extracting features that allow direct access to the relevant speech or music specific information is crucial. In addition to the conventional variance-based features, we utilize the higher-order moments of features, such as skewness and kurtosis. Moreover, we investigate the subband decomposition parameters in extracting features, which improves classification accuracy. Experiments on two speech/music datasets, which are publicly available, were performed and show that the higher-order moment features can improve classification accuracy when combined with the conventional variance-based features.

• International Journal of Contents
• /
• v.8 no.2
• /
• pp.1-6
• /
• 2012

Recently, content-based music information retrieval (MIR) systems for mobile devices have attracted great interest. However, music retrieval systems are greatly affected by background noise when music is recorded in noisy environments. Therefore, we evaluated various pre-processing methods using the Philips method to determine the one that performs most robust music retrieval in such environments. We found that dynamic noise reduction (DNR) is the best pre-processing method for a music retrieval system in noisy environments.

• International Journal of Computer Science & Network Security
• /
• v.23 no.9
• /
• pp.47-54
• /
• 2023

For Bengali music emotion classification, deep learning models, particularly CNN and RNN are frequently used. But previous researches had the flaws of low accuracy and overfitting problem. In this research, attention-based Conv1D and BiGRU model is designed for music emotion classification and comparative experimentation shows that the proposed model is classifying emotions more accurate. We have proposed a Conv1D and Bi-GRU with the attention-based model for emotion classification of our Bengali music dataset. The model integrates attention-based. Wav preprocessing makes use of MFCCs. To reduce the dimensionality of the feature space, contextual features were extracted from two Conv1D layers. In order to solve the overfitting problems, dropouts are utilized. Two bidirectional GRUs networks are used to update previous and future emotion representation of the output from the Conv1D layers. Two BiGRU layers are conntected to an attention mechanism to give various MFCC feature vectors more attention. Moreover, the attention mechanism has increased the accuracy of the proposed classification model. The vector is finally classified into four emotion classes: Angry, Happy, Relax, Sad; using a dense, fully connected layer with softmax activation. The proposed Conv1D+BiGRU+Attention model is efficient at classifying emotions in the Bengali music dataset than baseline methods. For our Bengali music dataset, the performance of our proposed model is 95%.

• Journal of the Korea Society of Computer and Information
• /
• v.24 no.6
• /
• pp.81-88
• /
• 2019

Based on the theory of planned behavior and the theory of legal deterrence, this study takes consumers' willingness to pay for digital music as the research object, investigates the consumers who have digital music consumption channels and behaviors, and discusses the willingness of consumers to pay for digital music and its influencing factors. The study attempts to achieve the following research purposes: First, explore the influencing factors of willingness to pay for digital music using domestic and foreign literature research and related content analysis. Second, we want to examine the effect of Attitude, Collective Specifications, Quality Sensitivity and Music affinity on willingness to pay. Third, Legal deterrence and resource availability tries to verify whether there is a moderating effect between Attitude, Collective Specifications, Quality Sensitivity and Music affinity and willingness to pay. The research data was collected in 2019 between April 6th to May 8th. Questionnaires were randomly distributed in fixed places, mainly in Hubei Province, China. A total of 393 questionnaires were selected for data analysis. Based on the previous theoretical review and empirical analysis, the study draws the following conclusions: Firstly, attitude, collective specifications, quality sensitivity and music affinity have an impact on the willingness to pay. Second, Legal deterrence has a regulatory effect on the relationship among quality sensitivity, musical affinity and the willingness to pay. Last the resource availability has a significant impact on the willingness to pay. It also has a regulatory effect on the relationship among quality sensitivity, music affinity and the willingness to pay.

• The Journal of the Acoustical Society of Korea
• /
• v.28 no.2
• /
• pp.112-118
• /
• 2009

This paper proposes a rapid detection algorithm of a salient region for music video browsing system, which can be applied to mobile device and digital video recorder (DVR). The input music video is decomposed into the music and video tracks. For the music track, the music highlight including musical chorus is detected based on structure analysis using energy-based peak position detection. Using the emotional models generated by SVM-AdaBoost learning algorithm, the music signal of the music videos is classified into one of the predefined emotional classes of the music automatically. For the video track, the face scene including the singer or actor/actress is detected based on a boosted cascade of simple features. Finally, the salient region is generated based on the alignment of boundaries of the music highlight and the visual face scene. First, the users select their favorite music videos from various music videos in the mobile devices or DVR with the information of a music video's emotion and thereafter they can browse the salient region with a length of 30-seconds using the proposed algorithm quickly. A mean opinion score (MOS) test with a database of 200 music videos is conducted to compare the detected salient region with the predefined manual part. The MOS test results show that the detected salient region using the proposed method performed much better than the predefined manual part without audiovisual processing.

• The Journal of the Acoustical Society of Korea
• /
• v.26 no.3E
• /
• pp.69-73
• /
• 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 the Korea Society of Computer and Information
• /
• v.21 no.4
• /
• pp.39-46
• /
• 2016

While the difficulty of the music can be classified by a variety of standard, conventional methods are classified by the subjective judgment based on the experience of many musicians or conductors. Music score is difficult to evaluate as there is no quantitative criterion to determine the degree of difficulty. In this paper, we propose a new classification method for determining the degree of difficulty of the music. In order to determine the degree of difficulty, we convert the score, which is expressed as a traditional music score, into electronic music sheet. Moreover, we calculate information about the elements needed to play sheet music by distance of notes, tempo, and quantifying the ease of interpretation. Calculating a degree of difficulty of the entire music via the numerical data, we suggest the difficulty evaluation of the score, and show the difficulty of music through experiments.