• Journal of Web Engineering
• /
• v.20 no.8
• /
• pp.2335-2360
• /
• 2021

A folksonomy is a classification system in which volunteers collaboratively create and manage tags to annotate and categorize content. The folksonomy has several problems in retrieving music using tags, including problems related to synonyms, different tagging levels, and neologisms. To solve the problem posed by synonyms, we introduced a mood vector with 12 possible moods, each represented by a numeric value, as an internal tag. This allows moods in music pieces and mood tags to be represented internally by numeric values, which can be used to retrieve music pieces. To determine the mood vector of a music piece, 12 regressors predicting the possibility of each mood based on acoustic features were built using Support Vector Regression. To map a tag to its mood vector, the relationship between moods in a piece of music and mood tags was investigated based on tagging data retrieved from Last.fm, a website that allows users to search for and stream music. To evaluate retrieval performance, music pieces on Last.fm annotated with at least one mood tag were used as a test set. When calculating precision and recall, music pieces annotated with synonyms of a given query tag were treated as relevant. These experiments on a real-world data set illustrate the utility of the internal tagging of music. Our approach offers a practical solution to the problem caused by synonyms.

• The KIPS Transactions:PartB
• /
• v.17B no.4
• /
• pp.263-274
• /
• 2010

To provide context-aware music recommendation service, first of all, we need to catch music mood that a user prefers depending on his situation or context. Among various music characteristics, music mood has a close relation with people‘s emotion. Based on this relationship, some researchers have studied on music mood detection, where they manually select a representative segment of music and classify its mood. Although such approaches show good performance on music mood classification, it's difficult to apply them to new music due to the manual intervention. Moreover, it is more difficult to detect music mood because the mood usually varies with time. To cope with these problems, this paper presents an automatic method to classify the music mood. First, a whole music is segmented into several groups that have similar characteristics by structural information. Then, the mood of each segments is detected, where each individual's preference on mood is modelled by regression based on Thayer's two-dimensional mood model. Experimental results show that the proposed method achieves 80% or higher accuracy.

• The Research Journal of the Costume Culture
• /
• v.23 no.5
• /
• pp.793-806
• /
• 2015

This study is to explore the effect of music characteristics (i.e., likeliness and familiarity of music) on the relationship between mood and attitude toward the product in the online shopping mall selling hand-made shoes. A total of 319 consumers participated in experiments with online shopping mall stimuli with a variety of background music. In results, consumer mood positively affected attitude toward the hand-made shoe products in the online shopping mall under background music. A moderating effect of music likeliness was found in the relationship between mood and product attitude, indicating that mood more strongly affected product attitude under more liked music than under less liked music. When consumers are listening to more liked music and are in good mood, they may build their attitudes toward products independently from their mood, whereas they may build positive attitude under good mood versus negative attitudes under bad mood if they are listening to less liked music. A moderating effect of music familiarity was not found in the relationship between mood and product attitude. Based on results, it was confirmed that the S-O-R model could be applied to explain the effect of background music on consumer responses in online shopping malls. Marketers may be able to select and adjust the likeliness and familiarity of background music to better serve consumers in diverse shopping conditions, referring to the study findings.

• Journal of Music and Human Behavior
• /
• v.16 no.2
• /
• pp.109-132
• /
• 2019

This study aimed to investigate the relationship between music listening and mood regulation. The effects of personality traits, music education, and sex on music use for mood regulation were also examined. Participants were 529 undergraduate students who completed the Korean Music in Mood Regulation Scale, Interpersonal Personality Item Pool (IPIP), Positive Affect and Negative Affect Schedule, and a questionnaire on their music education. Correlation analysis, multiple regression analysis, and t tests were conducted to explore the relationship among the measured variables. The results showed that music listening was related to enhancement of positive mood but not improvement of negative mood. Participants who had received extracurricular music education were more likely to use music listening as a strategy to regulate their mood than were participants without music education. Women were more likely to use music for mood regulation than were men. The multiple regression results indicate that individuals who rated themselves highly on Agreeableness and Openness to Experience on the IPIP were more likely to listen to music for mood regulation. These findings stress that music listening can be an effective strategy for mood regulation, which is critical for one's emotional well-being. It also indicates that effective music use as a mood regulation strategy varies depending on one's personal characteristics and history of music education.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.2
• /
• pp.807-824
• /
• 2016

In this paper, a method of mood classification based on user brainwaves is proposed for real-time application in commercial services. Unlike conventional mood analyzing systems, the proposed method focuses on classifying real-time user moods by analyzing the user's brainwaves. Applying brainwave-related research in commercial services requires two elements - robust performance and comfortable fit of. This paper proposes a filter based on Regularized Common Spatial Patterns (RCSP) and presents its use in the implementation of mood classification for a music service via a wireless consumer electroencephalography (EEG) device that has only 14 pins. Despite the use of fewer pins, the proposed system demonstrates approximately 10% point higher accuracy in mood classification, using the same dataset, compared to one of the best EEG-based mood-classification systems using a skullcap with 32 pins (EU FP7 PetaMedia project). This paper confirms the commercial viability of brainwave-based mood-classification technology. To analyze the improvements of the system, the changes of feature variations after applying RCSP filters and performance variations between users are also investigated. Furthermore, as a prototype service, this paper introduces a mood-based music list management system called MyMusicShuffler based on the proposed mood-classification method.

• Journal of muscle and joint health
• /
• v.16 no.2
• /
• pp.125-134
• /
• 2009

Purpose: The purpose of the study was to compare the effects of the Rhy-Kwon exercise with motion beat music program to the Rhy-Kwon with ordinary beat music program on physical fitness and mood in employed women. Method: Total of 29 women who did not have any particular disease were randomly assigned either to a Rhy-Kwon with motion beat music group or to a Rhy-Kwon with ordinary beat music group. Nine subjects in each group completed posttest measures (physical fitness and mood) in 8 weeks. Results: After the 8 weeks of the study period, there were significant improvement in sargent jump and mood especially for the subscale of vigor in the Rhy-Kwon with motion beat music group compared to their counterparts. But no significant differences were found between the groups in other physical fitness measures. Conclusion: The results showed that Rhy-Kwon with motion beat music program would partially improve physical fitness and mood (vigor). Further studies are needed to confirm the effects of Rhy-Kwon with motion beat music program with various populations.

• Journal of Korea Society of Industrial Information Systems
• /
• v.24 no.1
• /
• pp.13-21
• /
• 2019

The tendency of buyers of web information is changing from the cost-effectiveness which emphasizes the performance over the price to the cost-satisfaction which emphasizes the psychological satisfaction of the buyer. In music recommendation, one of the methods to increase psychological satisfaction is to use the music mood. In this paper, a music recommendation method considering the mood tag and the synonyms tag is proposed and, as an intermediate result of the proposed method, mood tags and music pieces are expressed in Thayer's AV space and then their distribution are analyzed. The analysis result shows the distributions of mood tags and the ones of music pieces are similar, which implies that the proposed recommendation method can provide significant results. In the future, the music recommendation performance will be analyzed.

• 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 IKEEE
• /
• v.13 no.2
• /
• pp.159-166
• /
• 2009

So far, many researches have been done to retrieve music information using static classification descriptors such as genre and mood. Since static classification descriptors are based on diverse content-based musical features, they are effective in retrieving similar music in terms of such features. However, human emotion or mood transition triggered by music enables more effective and sophisticated query in music retrieval. So far, few works have been done to evaluate the effect of human mood transition by music. Using formal representation of such mood transitions, we can provide personalized service more effectively in the new applications such as music recommendation. In this paper, we first propose our Emotion State Transition Model (ESTM) for describing human mood transition by music and then describe a music classification and recommendation scheme based on the ESTM. In the experiment, diverse content-based features were extracted from music clips, dimensionally reduced by NMF (Non-negative Matrix Factorization, and classified by SVM (Support Vector Machine). In the performance analysis, we achieved average accuracy 67.54% and maximum accuracy 87.78%.

• 한국HCI학회:학술대회논문집
• /
• 2006.02a
• /
• pp.400-405
• /
• 2006

In this paper, we present effective methods for music summarization which summarize music automatically. It could be used for sample music of on-line digital music provider or some music retrieval technology. When summarizing music, we use different two methods according to music length. First method is for finding sabi or chorus part of music which can be regarded as the most important part of music and the second method is for extracting several parts which are in different structure or have different mood in the music. Our proposed music summarization system is better than conventional system when structure of target music is explicit. The proposed method could generate just one important segment of music or several segments which have different mood in the music. Thus, this scheme will be effective for summarizing music in several applications such as online music streaming service and sample music for Tcommerce.