• The Journal of the Acoustical Society of Korea
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• v.39 no.4
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• pp.286-291
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• 2020

In this paper, we propose a system that recommends music through tempo-oriented music classification and sensor-based human activity recognition. The proposed method indexes music files using tempo-oriented music classification and recommends suitable music according to the recognized user's activity. For accurate music classification, a dynamic classification based on a modulation spectrum and a sequence classification based on a Mel-spectrogram are used in combination. In addition, simple accelerometer and gyroscope sensor data of the smartphone are applied to deep spiking neural networks to improve activity recognition performance. Finally, music recommendation is performed through a mapping table considering the relationship between the recognized activity and the indexed music file. The experimental results show that the proposed system is suitable for use in any practical mobile device with a music player.

• Journal of Korea Entertainment Industry Association
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• v.15 no.4
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• pp.101-111
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• 2021

This paper is a study that analyzes the use and techniques of rap in music that entered Melon Chart, one of the major music sites in Korea, during 2020. Among the songs that entered the chart, reflecting the public's preference, the trend of rap music, an important element of hip-hop music, is analyzed and musical features such as representative artists or frequently used rhythm patterns are analyzed. Through this paper, characteristics of rap music favored by the public and artists in recent popular music have been organized and formalized, and based on this, the elements of etude development for technical polishing are summarized. Through this, it is hoped that just like the long-time analyzed and structured performance techniques such as vocals and instruments, various styles of rap music can develop into a systematic and step-by-step technique system for students who want to learn and practice it.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.243-244
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• 2006

In this study, we propose a speech/music discrimination method using spectral peak track analysis. The proposed method uses the spectral peak track's duration at the same frequency channel for feature parameter. And use the duration threshold to discriminate the speech/music. Experiment result, correct discrimination ratio varies according to threshold, but achieved a performance comparable to another method and has a computational efficient for discrimination.

• Journal of the Korea Society of Computer and Information
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• v.22 no.7
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• pp.93-100
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• 2017

In this paper, we propose a methodology of using time-lag, make it serve the sound field, in order to smoothen the music production and reduce conflicts. The importance of music production in today's music industry chain is becoming more and more apparent. In the process of music production, the creators pay more attention to the design and adjustment of virtual sound field, especially the late mixing and production. In the process, as a commonly used tool for the adjustment of sound field, "time-lapse" plays a decisive role.

• Smart Media Journal
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• v.8 no.4
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• pp.64-71
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• 2019

The performance of an OMR (Optical Music Recognition) system is usually determined by the characterizing features of the input music score images. Low resolution is one of the main factors leading to degraded image quality. In this paper, we handle the low-resolution problem using the super-resolution technique. We propose the use of a deep neural network with instance normalization to improve the quality of music score images. We apply instance normalization which has proven to be beneficial in single image enhancement. It works better than batch normalization, which shows the effectiveness of shifting the mean and variance of deep features at the instance level. The proposed method provides an end-to-end mapping technique between the high and low-resolution images respectively. New images are then created, in which the resolution is four times higher than the resolution of the original images. Our model has been evaluated with the dataset "DeepScores" and shows that it outperforms other existing methods.

• 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.

• 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.

• Journal of the Korea Society of Computer and Information
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• v.13 no.7
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• pp.291-305
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• 2008

Previous study about Mobile Music Contents was mainly focused on Industry factors & enterprise factors like Technology. This study is not only focused on Technology factors, but also Customer & System factors like TAM of original, and also, this study first tried to find the Mobile Internet medium's customer purchase by each property's Mobile Music Contents from assorting the Mobile music Contents property and defining them. So to speak of this study have tried to concretely verify the factors of purchase. And also, I proposed an transformated model, and added independent variable factors, 'distinction', 'speed of system', 'speed of network', 'a career of use', 'amount of use', 'preservation', 'customization', 'information', 'confidence', 'omni presentation', 'potential possibility of reproduce'. By applying TAM this study has measured how the product property. user property, and system property causes effect to customer purchase of Mobile Music Contents. In results, First, success 8 factors were determined to be the purchase of 'Ringtone'. Second, 8 factors were determined to be the purchase of 'Ring-Back tone', but, 'The use of convenience' was not influenced 'Intention of Purchase'. Third, 6 factors were determined to be the purchase of 'Full Track Download of Music'. At the Conclusion this study presented a scheme that these study results could be applied in actual company and academic world.

• Journal of the Korea Society of Computer and Information
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• v.25 no.6
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• pp.25-33
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• 2020

In this paper, the purpose is to verify the impact of performance expectations, effort expectations, social impact, individual innovation and perceived value on the intent of use and the behavior of use. Used Unified Theory of Acceptance and Use of Technology (UTAUT) to verify the applicability of this model in China, and established the research model by adding two new variables to UTAUT according to the situation of the Chinese market. To achieve this goal, 345 questionnaires were collected for experienced music creators using artificial intelligence nuggets in China by means of Internet research. The collected data were analyzed through frequency analysis, factor analysis, reliability analysis, and structural equation analysis through SPSS V. 22.0 and AMOS V 22.0. The verification of the hypotheses presented in the research model identified the decisive influence factors on the use of artificial intelligence music acceptance by Chinese users. The study is innovative in that it attempts to verify the applicability of UTAUT in the Chinese context. In the construction of the user acceptance model of AI music, three influencing factors will have an effect on users' intentions, and according to the degree of effect, from largest to smallest, they are respectively Perceived Innovativeness, Performance Expectancy and Effort Expectancy. This paper will also provide some management advices, i.e. improving the utility and usability of AI music, encouraging users with individual innovativeness, developing competitive and attractive pricing policies, increasing publicity, and prioritizing word-of-mouth advertising.

• Journal of Oral Medicine and Pain
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• v.31 no.1
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• pp.37-45
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• 2006

The use of music as a means of inducing positive emotions and subsequent relaxation has been extensively studied by researchers. A great deal of this research has focussed on the use of music as a means of reducing feelings of anxiety and stress as well as aiding in the relief of numerous pathologies. The purpose of this study was to evaluate this effect of music using laser doppler flowmeter that monitors relative changes in the muscular blood cell perfusion, concentration of moving blood cells, mean velocity of the myofascial pain disorder patients. Total of 30 subjects were selected. They were monitored by LDF(Laser Doppler Flowmeter) during music listening for 3 minutes. LDF probe was placed over the temporalis muscle. The obtained results were as follows: 1. The average blood perfusion for 3 minutes of music listening at surface of temporalis muscles were elevated on both affected and unaffected parts compared to rest time. 2. The average of blood perfusion elevated during music listening at surface of temporalis muscles were higher in affected part than unaffected part. 3. Increase of blood perfusion during music listening is proportioin to that before music listening. In summary, these results may encouraging start to the elucidation of the hemodynamic events occuring during music listening and aimed to serve as a base for further studies.