• Journal of the Korea Society of Computer and Information
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• v.20 no.10
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• pp.69-76
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• 2015

User's selection of music is largely influenced by private tastes as well as emotional states, and it is the unconsciousness projection of user's emotion. Therefore, we think user's emotional states to be music itself. In this paper, we try to grasp user's emotional states from music selected by users at a specific context, and we analyze the correlation between its context and user's emotional state. To get emotional states out of music, the proposed method extracts emotional words as the representative of music from lyrics of user-selected music through morphological analysis, and learns weights of linear classifier for each emotional features of extracted words. Regularities learned by classifier are utilized to calculate predictive weights of virtual music using weights of music chosen by other users in context similar to active user's context. Finally, we propose a method to recommend some pieces of music relative to user's contexts and emotional states. Experimental results shows that the proposed method is more accurate than the traditional collaborative filtering method.

• Journal of the Korea Society of Computer and Information
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• v.15 no.11
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• pp.91-99
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• 2010

Recently, recognizing user resource for personalized service has been needed in digital content service fields. Especially, to analyze user taste, recommend music and service music related information need recognition of user music file in case of online music service. Music related information service is offered through recognizing user music based on tag information. Recognition error has grown by weak points like changing and removing of tag information. Techniques of content based user music recognition with music signal itself are researched for solving upper problems. In this paper, we propose user music recognition on the internet by extracted feature from music signal. Features are extracted after suitable preprocessing for structure of content based user music recognition. Recognizing on music server consist of feature form are progressed with extracted feature. Through this, user music can be recognized independently of tag data. 600 music was collected and converted to each 5 music qualities for proving of proposed recognition. Converted 3000 experiment music on this method is used for recognition experiment on music server including 300,000 music. Average of recognition ratio was 85%. Weak points of tag based music recognition were overcome through proposed content based music recognition. Recognition performance of proposed method show a possibility that can be adapt to online music service in practice.

• Journal of the Korea Society of Computer and Information
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• v.24 no.7
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• pp.45-52
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• 2019

With the popularity of mobile Internet and smart phones, the domestic digital music access platform has entered a period of rapid development. Existing studies in the academic circle have shown that experience has a positive impact on user perception and loyalty. However, the research on the relationship between brand loyalty, user perception and user experience of Internet products has not received much attention. Starting from the brand loyalty theory and user experience theory, this paper explores the mechanism of user experience, user perception and brand loyalty of digital music access platform. Based on the development status of digital music access platform in China, the purpose is to explore how the user experience of the mainstream digital music access platform can affect Usefulness Perception, Epidemic Perception. And to explore how the Usefulness Perception, Epidemic Perception can affect users' brand loyalty. also further explore the role of users' perception in this process to understand the relationship between brand and user experience. In practical operations, 398 formal questionnaires were issued online to collect first-hand data, and reliability analysis, factor analysis, correlation analysis and hypothesis analysis were carried out successively on the data in the later stage. Through research, it is found that the user experience of digital music access platform has a partial significant impact on the perception and loyalty of applications. The visual presentation and emotional feelings of digital music access platform are positively correlated with the perceived usefulness of applications. The visual presentation and emotional feelings of digital music access platform are positively correlated with the perceived popularity of applications. The e perceived usefulness and perceived popularity of the digital music access platform are positively correlated with the loyalty of the application. Through this research, it has certain guiding significance to the promotion of digital music access platform's brand loyalty degree.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.707-710
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• 2009

In this paper, we introduce an interface for exploring music using emotional model. First, we survey arousal-valence factors of various music and calculate a correlation between audio fefatures of music and arousal-valence factors to build an AV model. Then, various music is aligned and arranged using the AV model and the user can explore music in this interface. To select the desired music more intuitively, we introduce new fade in/out function based on the location of the user's mouse point. We also offer several mode of selecting music so user can explore music using most suitable mode of interface. With our interface, the user can find the emotionally desired music more easily.

• Journal of Multimedia Information System
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• v.4 no.3
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• pp.99-106
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• 2017

This study does a qualitative and quantitative analysis of how music by humming is being used by music educators in the classroom. Music by humming is part division of music information retrieval. In order to define what a music information retrieval system is first I need to define what it is. Berger and Lafferty (1999) define information retrieval as "someone doing a query to a retrieval system, a user begins with an information need. This need is an ideal document- perfect fit for the user, but almost certainly not present in the retrieval system's collection of documents. From this ideal document, the user selects a group of identifying terms. In the context of traditional IR, one could view this group of terms as akin to expanded query." Music Information Retrieval has its background in information systems, data mining, intelligent systems, library science, music history and music theory. Three rounds of surveys using question pro where completed. The study found that there were variances in knowledge, training and level of awareness of query by humming, music information retrieval systems. Those variance relationships where based on music specialty, level that they teach, and age of the respondents.

• Journal of the Korea Society of Computer and Information
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• v.29 no.5
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• pp.177-187
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• 2024

177-Existing music recommendation systems do not sufficiently consider the discrepancy between the intended emotions conveyed by song lyrics and the actual emotions felt by users. In this study, we generate topic vectors for lyrics and user comments using the LDA model, and construct a user preference model by combining user behavior trajectories reflecting time decay effects and playback frequency, along with statistical characteristics. Empirical analysis shows that our proposed model recommends music with higher accuracy compared to existing models that rely solely on lyrics. This research presents a novel methodology for improving personalized music recommendation systems by integrating emotion recognition and user behavior analysis.

• Journal of Internet Computing and Services
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• v.16 no.6
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• pp.133-141
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• 2015

Mass multimedia contents are shared through various social media servies including social network service. As social network reveals user's current situation and interest, highly satisfactory personalized recommendation can be made when such features are applied to the recommendation system. In addition, classifying the music by emotion and using analyzed information about user's recent emotion or current situation by analyzing user's social network, it will be useful upon recommending music to the user. In this paper, we propose a music recommendation method that makes an emotion model to classify the music, classifies the music according to the emotion model, and extracts user's current emotional state represented on the social network to recommend music, and evaluates the validity of our method through experiments.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.4
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• pp.195-200
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• 2015

Recently, most people use android based smartphones and we can find music players in any smartphones. However, it's hard to find a personalized music player which applies user's preference. In this paper, we propose an emotion-based music player, which analyses and classifies the music with user's emotion, recommends the music, applies the user's preference, and visualizes the music by color. Through the proposed music player, user could be able to select musics easily and use an optimized application.

• Speech Sciences
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• v.15 no.4
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• pp.121-130
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• 2008

This paper proposes the way of improvement and system build-up for shop music broadcasting services provided by the Internet. Comparing the shop music broadcasting services and personal music broadcasting services, we propose the way of shop music broadcasting services customers prefer to. That is, such a function is provided that a user can control the broadcasting music lists a specialist provides according to the current circumstance of shop. This paper proposes the whole system such a service is possible and verifies the efficiency by experiments.

• Informatization Policy
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• v.30 no.3
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• pp.92-121
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• 2023

As music streaming services (MSS) involve various interactions with users during the music consumption process, it is important to understand the user experience and manage the service accordingly. This study developed a user experience model for MSS by theoretically linking the quality characteristics considered important by music service users with the structure of user experience. PLS analysis was then performed using survey data to test the model. As a result, functionality (search, browsing, and personalized recommendation), UI usability, content quality (currentness, sufficiency, relevance), and monetary cost were found to be key experience factors that determine the experience consequence, i.e., user satisfaction. In addition, in a supplementary analysis comparing domestic and global services, differences in user experience were found between the two groups in terms of functionality and content quality. The user experience model of MSS proposed in this study serves as a new foundation for theory-based research in this field and provides meaningful implications for the competitive landscape among music streaming service platforms and for their competitive strategies.