• Korean Management Science Review
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• v.25 no.2
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• pp.13-23
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• 2008

Top music 10 is a additional service product of the A mobile telephone company. Up to now, A company is just selling it by outbound TM to customers which visit any contents of Top Music 10. In this paper, we proposed a targeting method combining two score models by data mining. The proposed combining model is to find customers more likely to respond to outbound TM. The proposed targeting method is expected to improve both from 32.8% to 44.0% in the response rate and from 54.7% to 61.4% in the retention rate.

• Information Systems Review
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• v.16 no.3
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• pp.49-68
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• 2014

These days, consumers have increasingly preferred to digital real-time streamlining and downloading to listen to music because this is convenient and affordable for the consumers. Accordingly, sales of music in compact disk formats have steadily declined. In this regards, online digital music has become a new communication channel to listen musics, where digital files can be delivered over various online networks to people's computing devices. The majority of online digital music distributors has Music Recommender Systems for sales of digital music on their websites. Music Recommender Systems are parts of information filtering systems that provide the ratings or preferences that users give to music. Korean online digital music distributors have Music Recommender Systems. But those online music distributors didn't provide any rules or clear procedures that recommend music. Therefore, we raise important questions as follows: "Is Music Recommender Systems Fair?", "What is the impact of Music Recommender Systems on online music rankings and sales?" While previous studies have focused on usefulness of Music Recommender Systems, this study investigates not only fairness of Current Music Recommender Systems but also Relationship between Music Recommender Systems and online Music Charts. This study examines these issues based on Bandwagon effect, ranking effect, Slot effect theories. For our empirical analysis, we selected the most famous five online digital music distributors in terms of market shares. We found that all recommended music is exposed to the top of 'daily music charts' in online digital music distributors' websites. We collected music ranking data and recommended music data from 'daily music chart' during a one month. The result shows that online music recommender systems are not fair, since they mainly recommend particular music that supported by a specific music production company. In addition, the recommended music are always exposed to the top of music ranking charts. We also find that recommended music usually appear at the top 20 ranking charts within one or two days. Also, the most music in the top 50 or 100 ranks are the recommended music. Moreover, recommended music usually remain the ranking charts more than one month while non-recommended music often disappear at the ranking charts within two week. Our study provides an important implication to online music industry. Because music recommender systems and music ranking charts are closely related, music distributors may improperly use their recommender systems to boost the sales of music that related to their own companies. Therefore, online digital music distributor must clearly announce the rules and procedures about music recommender systems for the better music industry.

• The Journal of the Korea Contents Association
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• v.22 no.7
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• pp.227-239
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• 2022

This study conducted an exploratory empirical analysis on the factors affecting the performance of digital classical music based on signaling theory. For this purpose, using the classical weekly chart provided by the music platform Genie, 297 digital music sources that entered the top 100 chart from March 2020 to October 2020 (35 weeks). In this study, as signals that can influence consumers' choice to listen to classical nusic, we set an the artist's award history, artist's broadcast content linkage, taking the top spot in the first classical music chart entry, producing companies' competency, and the popularity of classical music repertoire. The effect of these signals on the chart success of digital classical music was verified subsequently. As a result of the verification, it was found that the artist's broadcast content linkage, taking the top spot in the first classical music chart entry, and the popularity of the classical music repertoire indeed had a positive effect on the chart success of a classical music. On the other hand, the artist's award history and the producing companies' competence did not significantly affect the chart success of digital classical music. This study is the first empirical study on the success factors of digital classical music performed from a business perspective, and is expected to contribute to subsequent studies related to classical music.

• Journal of Korea Multimedia Society
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• v.10 no.11
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• pp.1398-1406
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• 2007

Providing natural and efficient access to the fast growing multimedia information is a critical aspect for content-based information system. Query by humming system allows the user to find a song by humming part of the tune form music database. Conventional music information retrieval systems use a high precision pitch extraction method. However, it is very difficult to extract true pitch perfectly. So, In this paper, we propose to use YIN parameter with applying the reliability to reduce the pitch extraction errors. And we describes developed music information retrieval method based on a query by humming system which uses reliable feature extraction. Developed system is based on a continuous dynamic programming algorithm with features including pitch, duration and energy along with their confidence measures. The experiment showed that the proposed method could reduce the errors of the top-10 7.2% and the top-1 9.1% compared with the cepsturm based multiple pitch candidate. The overall retrieval system achieved 92.8% correct retrieval in the top-10 rank list on a database of 155 songs.

• The Journal of the Korea Contents Association
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• v.15 no.6
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• pp.157-167
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• 2015

This research studied how idol music popularity influences on equilibrium of ecosystem in Korean popular music market. Because most of media & press reported that idol music caused unbalance of whole market so this research issued it. Research method is borrowed production of cultural perspective of Peterson and subject of analysis is selected idol music from Melon weekly music chart(2000-2014), which is No. 1 digital music distributor. The result showed idol music did not bring unbalance of ecosystem structure in Korean polular music market and found it contributed diversity of music genre in music market by introducing various musical style all the more. This kind of untested information should not cause idol music production leading new Korean Wave to shrink. Government related organization has to do policy making based on verified fact in future and it should be handled as important matter for sustainable and expansive reproduction of Korean wave.

• Journal of Information Processing Systems
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• v.20 no.1
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• pp.13-23
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• 2024

Music brings pleasure and relaxation to people. Therefore, it is necessary to classify musical genres based on scenes. Identifying favorite musical genres from massive music data is a time-consuming and laborious task. Recent studies have suggested that machine learning algorithms are effective in distinguishing between various musical genres. However, meeting the actual requirements in terms of accuracy or timeliness is challenging. In this study, a hybrid machine learning model that combines a deep residual auto-encoder (DRAE) and support vector machine (SVM) for musical genre recognition was proposed. Eight manually extracted features from the Mel-frequency cepstral coefficients (MFCC) were employed in the preprocessing stage as the hybrid music data source. During the training stage, DRAE was employed to extract feature maps, which were then used as input for the SVM classifier. The experimental results indicated that this method achieved a 91.54% F1-score and 91.58% top-1 accuracy, outperforming existing approaches. This novel approach leverages deep architecture and conventional machine learning algorithms and provides a new horizon for musical genre classification tasks.

• Journal of Information Processing Systems
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• v.13 no.5
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• pp.1213-1228
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• 2017

The pitch tracking of music has been researched for several decades. Several possible improvements are available for creating a good t-distribution, using the instantaneous robust algorithm for pitch tracking framework to perfectly detect pitch. This article shows how to detect the pitch of music utilizing an improved detection method which applies a statistical method; this approach uses a pitch track, or a sequence of frequency bin numbers. This sequence is used to create an index that offers useful features for comparing similar songs. The pitch frequency spectrum is extracted using a modified instantaneous robust algorithm for pitch tracking (IRAPT) as a base combined with the statistical method. The pitch detection algorithm was implemented, and the percentage of performance matching in Thai classical music was assessed in order to test the accuracy of the algorithm. We used the longest common subsequence to compare the similarities in pitch sequence alignments in the music. The experimental results of this research show that the accuracy of retrieval of Thai classical music using the t-distribution of instantaneous robust algorithm for pitch tracking (t-IRAPT) is 99.01%, and is in the top five ranking, with the shortest query sample being five seconds long.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.12
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• pp.561-568
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• 2018

This thesis focuses on the five functions of film music as proposed by American composer Aaron Copland, and the application of these functions in the modern film Cinema Paradiso featuring music by Italian composer Ennio Morricone. Morricone has shown the application of these techniques in Western films such as the 1960s film A Fistful of Dollars and the top-rated 1980s films The Mission and Cinema Paradiso. Through his work, he maximizes the effects of representation in each scene by using variations of the theme music to effectively apply the functional theory of film music. Cinema Paradiso, especially, pursued the consistency and diversity of film at the same time. And it emphasized the specific atmosphere, intending to provide continuity in the scenes during the progress of the film. This thesis provides an understanding of the variation technique in film music, and the functionality of film music from scene to scene, as used by Morricone, for students majoring in film music.

• Journal of Digital Contents Society
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• v.18 no.6
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• pp.1025-1032
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• 2017

Recently the domestic popular song market has been growing mainly in digital sound sources. As a result of analyzing the top 100 music charts from 2012 to 2016 through digital sound sources and musical scores, the average annual BPM has fallen by 11.26 over five years. Every year, The style of music has diversified every year, and the proportion of Hip-hop has doubled from 8.5% in 2012 to 17.8% in 2015. Dance music and ballads have a high preference rate, but the relationship is inversely proportional. Singer composition was inversely proportional to the ratio of female solo to male group. Especially, the relationship between BPM and the Major/Minor key is that 81.42% for slow tempo songs is Major key and 53.85% for fast tempo songs is minor key. In the case of TV drama OST, the solo singer 's music was preferred, the music style was 80% pop and 20% ballad.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.171-191
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• 2020

This study focused on digital music, which is the most valuable cultural asset in the modern society and occupies a particularly important position in the flow of the Korean Wave. Digital music was collected based on the "Gaon Chart," a well-established music chart in Korea. Through this, the changes in the ranking of the music that entered the chart for 73 weeks were collected. Afterwards, patterns with similar characteristics were derived through time series cluster analysis. Then, a descriptive analysis was performed on the notable features of each pattern. The research process suggested by this study is as follows. First, in the data collection process, time series data was collected to check the ranking change of digital music. Subsequently, in the data processing stage, the collected data was matched with the rankings over time, and the music title and artist name were processed. Each analysis is then sequentially performed in two stages consisting of exploratory analysis and explanatory analysis. First, the data collection period was limited to the period before 'the music bulk buying phenomenon', a reliability issue related to music ranking in Korea. Specifically, it is 73 weeks starting from December 31, 2017 to January 06, 2018 as the first week, and from May 19, 2019 to May 25, 2019. And the analysis targets were limited to digital music released in Korea. In particular, digital music was collected based on the "Gaon Chart", a well-known music chart in Korea. Unlike private music charts that are being serviced in Korea, Gaon Charts are charts approved by government agencies and have basic reliability. Therefore, it can be considered that it has more public confidence than the ranking information provided by other services. The contents of the collected data are as follows. Data on the period and ranking, the name of the music, the name of the artist, the name of the album, the Gaon index, the production company, and the distribution company were collected for the music that entered the top 100 on the music chart within the collection period. Through data collection, 7,300 music, which were included in the top 100 on the music chart, were identified for a total of 73 weeks. On the other hand, in the case of digital music, since the cases included in the music chart for more than two weeks are frequent, the duplication of music is removed through the pre-processing process. For duplicate music, the number and location of the duplicated music were checked through the duplicate check function, and then deleted to form data for analysis. Through this, a list of 742 unique music for analysis among the 7,300-music data in advance was secured. A total of 742 songs were secured through previous data collection and pre-processing. In addition, a total of 16 patterns were derived through time series cluster analysis on the ranking change. Based on the patterns derived after that, two representative patterns were identified: 'Steady Seller' and 'One-Hit Wonder'. Furthermore, the two patterns were subdivided into five patterns in consideration of the survival period of the music and the music ranking. The important characteristics of each pattern are as follows. First, the artist's superstar effect and bandwagon effect were strong in the one-hit wonder-type pattern. Therefore, when consumers choose a digital music, they are strongly influenced by the superstar effect and the bandwagon effect. Second, through the Steady Seller pattern, we confirmed the music that have been chosen by consumers for a very long time. In addition, we checked the patterns of the most selected music through consumer needs. Contrary to popular belief, the steady seller: mid-term pattern, not the one-hit wonder pattern, received the most choices from consumers. Particularly noteworthy is that the 'Climbing the Chart' phenomenon, which is contrary to the existing pattern, was confirmed through the steady-seller pattern. This study focuses on the change in the ranking of music over time, a field that has been relatively alienated centering on digital music. In addition, a new approach to music research was attempted by subdividing the pattern of ranking change rather than predicting the success and ranking of music.