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

• ETRI Journal
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• v.33 no.6
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• pp.871-879
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• 2011

Because a wide variety of multimedia services are provided through personal wireless communication devices, the demand for efficient bandwidth utilization becomes stronger. This demand naturally results in the introduction of the variable bitrate speech coding concept. One exemplary work is the selectable mode vocoder (SMV) that supports speech/music classification. However, because it has severe limitations in its classification performance, a couple of works to improve speech/music classification by introducing support vector machines (SVMs) have been proposed. While these approaches significantly improved classification accuracy, they did not consider correlations commonly found in speech and music frames. In this paper, we propose a novel and orthogonal approach to improve the speech/music classification of SMV codec by adaptively tuning SVMs based on interframe correlations. According to the experimental results, the proposed algorithm yields improved results in classifying speech and music within the SMV framework.

• Journal of the Korean Data and Information Science Society
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• v.18 no.1
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• pp.107-121
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• 2007

An efficient encoding algorithm (or encryption algorithm) is essential for mobile devices since their resources such as computation power and battery capacity are very limited. This study is to propose an efficient encoding scheme for protecting mobile music. In the proposed scheme, server distributes each music file in a shuffled form or an encrypted one, then only authorized consumers can play the music after un-shuffling or decrypting it. We show the effectiveness of our proposed scheme by implementing and evaluating the prototype system on WIPI emulator. Experimental results show that our scheme can achieve much better performance than the standard encryption algorithm of OMA DRM.

• 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.

• The Journal of Korea Robotics Society
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• v.8 no.3
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• pp.217-227
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• 2013

We introduce a learning system for the sight reading of simple drum sequences. Sight reading is a cognitive-motor skill that requires reading of music symbols and actions of multiple limbs for playing the music. The system provides knowledge of results (KR) pertaining to the learner's performance by color-coding music symbols, and guides the learner by indicating the corresponding action for a given music symbol using additional auditory or vibrotactile cues. To evaluate the effects of KR and guidance cues, three learning methods were experimentally compared: KR only, KR with auditory cues, and KR with vibrotactile cues. The task was to play a random 16-note-long drum sequence displayed on a screen. Thirty university students learned the task using one of the learning methods in a between-subjects design. The experimental results did not show statistically significant differences between the methods in terms of task accuracy and completion time.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.6
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• pp.148-155
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• 2008

In this paper, we constructed the noise robust content-based music retrieval system in mobile environment. The performance of the proposed system was verified with ZCPA feature which is blown to have noise robust characteristic in speech recognition application. In addition, new indexing and fast retrieval method are proposed to improve retrieval speed about 99% compare to exhaustive retrieval for large music DB. From the computer simulation results in noise environment of 15dB - 0dB SNR, we confirm the superior performance of the proposed system about 5% - 30% compared to MFCC and FBE(filter bank energy) feature.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.1 s.307
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• pp.85-90
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• 2006

In this paper, we propose the content-based music information retrieval system with improved retrieval speed and stable performance while maintaining resonable retrieval accuracy In order to solve the in-stable system problem multi-feature clustering (MFC) is used to setup robust music DB. In addition, the music retrieval speed was improved by using the Superclass concept. Effectiveness of the system with SuperClass and without SuperClass is compared in terms of retrieval speed, accuracy and retrieval precision. It is demonstrated that the use of WC and Superclass substantially improves music retrieval speed up to $20\%\~40\%$ while maintaining almost equal retrieval accuracy.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.24 no.1
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• pp.64-72
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• 2013

Blind equalization is a technique that equalizes the received signals without the training sequence. Because of the absence of training sequence, we can increase the bandwidth efficiency due to the blind equalization system. And we must use the blind equalization for removing the ISI in mobile satellite communication receiver. ISI occurs due to mobility of users in mobile satellite communications. Blind equalization is suitable for the mobile satellite communication channels. In this blind equalization, it's very important to improve BER performance to apply the mobile satellite communication system. In this paper, we propose the blind beamforming equalization system using the beamforming, MUSIC algorithm and coordinate change method. We were confirmed by the simulation that the proposed system improves the BER performance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.1
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• pp.27-32
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• 2019

Research that implements the classification process through Deep Learning algorithm, one of the outstanding human abilities, includes a unimodal model, a multi-modal model, and a multi-modal method using music videos. In this study, the results were better by suggesting a system to analyze each song's spectrum into short samples and vote for the results. Among Deep Learning algorithms, CNN showed superior performance in the category of music genre compared to RNN, and improved performance when CNN and RNN were applied together. The system of voting for each CNN result by Deep Learning a short sample of music showed better results than the previous model and the model with Softmax layer added to the model performed best. The need for the explosive growth of digital media and the automatic classification of music genres in numerous streaming services is increasing. Future research will need to reduce the proportion of undifferentiated songs and develop algorithms for the last category classification of undivided songs.

• Journal of the Korean Society of Physical Medicine
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• v.14 no.4
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• pp.143-152
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• 2019

PURPOSE: This examined the effects of a sling exercise based on music on the cognition, physical performance of patients with dementia. METHODS: Thirty subjects with dementia volunteered to participate in this study. All subjects were allocated randomly to either the experimental group or control group, with 15 subjects in each group. All subjects underwent the exercise program for an average of 60 minutes per day for 16 weeks. The experimental group performed sling exercise based on music, and the control group performed the general exercise program. Assessments were made using the Korean version of mini-mental state examination (MMSE-K), 10 m walk test (10MWT), Tinetti mobility test (TMT), and Katz's Index of Independence in activity daily living (KIIADL) to detect changes in the cognitive level and physical performance before and after the 16-week training period. A paired t-test was conducted to compare the within-group change before and after the intervention. An independent t-test was performed to compare the between-group difference. The statistical significance level was set to α=.05 for all variables. RESULTS: The experimental group showed significant within-group changes in the MMSE-K, 10MWT, TMT, and KIIADL (p<.05). The control group showed a significant change in only the KIIADL (p<.05). A significant difference was observed between the experimental group and the control group regarding the change in MMSE-K and KIIADL after the interventions (p<.05). CONCLUSION: A music-based sling exercise program effectively improves cognition, physical performance, and ADL in patients with dementia. Further studies with a wider range of subjects and scientific equipment will be needed to strengthen the results of this study.