• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권2호
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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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• 제33권6호
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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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• 제18권1호
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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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• 제13권5호
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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.

• 로봇학회논문지
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• 제8권3호
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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.

• 대한전자공학회논문지SP
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• 제45권6호
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• pp.148-155
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• 2008

본 논문에서는 모바일 환경에서 적용 가능한 잡음에 강인한 내용기반 음악 검색 시스템을 구축하였다. 제안된 시스템은 기존의 음성인식 분야에서 잡음에 강인한 특성을 가진 것으로 알려진 ZCPA 특징을 내용기반 음악 검색 시스템에 적용시켜 그 성능을 검증하였다. 또한 본 논문에서는 대용량 음악 DB 검색에서 기존의 전수(Exhaustive) 검색에 비해 검색 속도를 99% 가까이 개선할 수 있는 새로운 인덱싱 방법과 고속 검색 알고리즘을 제안하였다. 신호대 잡음비가 15dB - 0dB인 잡음 환경에서의 모의실험 결과, 제안 시스템은 기존의 MFCC와 필터뱅크 에너지 특징에 비해 약 5% - 30% 정도의 우수한 성능을 나타냄을 확인하였다.

• 대한전자공학회논문지SP
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• 제43권1호
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• pp.85-90
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• 2006

본 논문에서는 빠르고 안정적이면서도 높은 검색 성공률을 보장하는 내용기반 음악 정보 검색 시스템을 구축하였다. 시스템 질의 구간이나 질의 길이에 따른 시스템 불안정성 문제를 해결할 수 있는 DB 구축 방법인 MFC기법과 각 Superclass별로 특징 벡터의 차수를 차등 적용하여 시스템의 검색 속도를 향상시킬 수 있는 기법을 적용하였다. Superclass를 적용한 시스템은 SuperClass를 적용하지 않은 시스템과의 검색 성공률, 검색 속도 그리고 검색 Precision 비교 실험에서 대등한 성능을 유지하면서 검색 속도를 $20\%\~40\%$ 향상시켰다.

• 한국전자파학회논문지
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• 제24권1호
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• pp.64-72
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• 2013

블라인드 등화기법은 트레이닝 시퀀스 없이 수신된 신호만을 가지고 채널 등화를 할 수 있다. 블라인드 등화시스템은 트레이닝 시퀀스가 존재하지 않기 때문에 블라인드 등화 시스템을 통해서 대역 효율을 향상시킬 수 있는 장점이 있습니다. 이 때문에 이동 위성 통신에서 유저들의 이동으로 인해서 발생하는 ISI를 제거하기 위해서 블라인드 등화시스템을 사용해야 합니다. 트레이닝 시퀀스를 사용하지 않기 때문에 우리는 블라인드 등화기시스템을 통해 대역 효율을 증가시킬 수 있으며, 트레이닝 시퀀스를 사용하지 않는 이동 통신의 수신기에서는 블라인드 등화를 사용해야 합니다. 블라인드 등화는 이동 위성 통신 채널에 적합합니다. 이 블라인드 등화를 이동 위성 통신 시스템에 적용시켜 BER 성능을 향상시키는 것은 매우 중요합니다. 본 논문에서는 MCMA와 SAG MCMA를 적용한 블라인드 등화 시스템을 제안한다. 이 시스템은 빔포밍과 MUSIC 알고리즘 그리고 coordinate change 기법을 적용해서 BER 성능이 향상됨을 시뮬레이션을 통해서 확인할 수 있다.

• 한국정보통신학회논문지
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• 제23권1호
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• pp.27-32
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• 2019

인간이 가진 뛰어난 능력 중의 하나인 곡 분류 과정을 딥러닝 알고리즘을 통해 구현하는 연구는 단일데이터를 이용한 유니모달 모델, 멀티모달 모델, 뮤직비디오를 이용한 멀티모달 방식 등이 있다. 이 연구에서는 곡의 스펙트로그램을 짧은 샘플들로 분할하여 각각을 CNN으로 분석한 뒤 그 결과를 투표하는 시스템을 제안하여 더 좋은 결과를 얻었다. 딥러닝 알고리즘 중 CNN이 RNN에 비해 음악 장르 구분에 있어 우수한 성능을 보였으며 CNN과 RNN을 같이 적용했을 때 성능이 좋아짐을 알 수 있었다. 음악샘플을 나누어 각각의 CNN 결과를 투표하는 시스템이 이전 모델에 비해 좋은 결과를 나타내었고 이 모델에 Softmax 레이어를 추가한 모델이 가장 좋은 성능을 보였다. 디지털 미디어의 폭발적인 성장과 수많은 스트리밍 서비스 속에서 음악장르의 자동분류에 대한 필요는 점점 증가하고 있는 추세이다. 향후 연구에서는 미분류 곡의 비율을 낮추고 최종적으로 미분류된 곡들의 장르구분에 대한 알고리즘을 개발할 필요가 있을 것이다.

• 대한물리의학회지
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• 제14권4호
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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.