• 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.

• The Journal of the Acoustical Society of Korea
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• v.28 no.5
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• pp.471-476
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• 2009

In this paper, we apply a discriminative weight training to a support vector machine (SVM) based speech/music classification for the selectable mode vocoder (SMV) of 3GPP2. In our approach, the speech/music decision rule is expressed as the SVM discriminant function by incorporating optimally weighted features of the SMV based on a minimum classification error (MCE) method which is different from the previous work in that different weights are assigned to each the feature of SMV. The performance of the proposed approach is evaluated under various conditions and yields better results compared with the conventional scheme in the SVM.

• The Journal of the Acoustical Society of Korea
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• v.43 no.2
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• pp.200-206
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• 2024

Computing music similarity is an indispensable component in developing music search service. This paper proposes a relevance weight of each chromagram vector for cover song identification in computing a music similarity function in order to boost identification accuracy. We derive a music similarity function using the relevance weight based on the probabilistic relevance model, where higher relevance weights are assigned to less frequently-occurring discriminant chromagram vectors while lower weights to more frequently-occurring ones. Experimental results performed on two cover music datasets show that the proposed music similarity improves the cover song identification performance.

• The Journal of Society for e-Business Studies
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• v.22 no.3
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• pp.75-85
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• 2017

Personalization recommendations have already proven in many areas of the e-commerce industry. For personalization recommendations, additional work such as reclassifying items is generally necessary, which requires personal information. In this study, we propose a recommendation technique that neither exploit personal information nor reclassify items. We focus on music recommendation and performed experiments with actual music listening data. Experimental analysis shows that the proposed method may result in meaningful recommendations albeit it exploits less amount of data. We analyze the appropriate number of items and present future considerations for contextual recommendation.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.357-358
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• 2013

의학의 발달로 인해 전세계적으로 인구의 고령화가 진행되어 노인 인구가 차지하는 비중이 갈수록 증가하고 있다. 본 논문은 고령자를 위한 작품으로 카메라와 FPGA Board, Touch Panel을 유기적으로 결합하여 음악감상과 운동효과를 가져올 수 있는 게임을 포함한 하드웨어기반 시스템을 소개한다. 간단히 공을 화면에 맞추는 게임과 손 모양 인식에 따라 음악을 제어할 수 있는 부분의 설계와 알고리즘을 기술하고 있다. 본 시스템은 노인들에게 편리하고 간단한 UI를 제공하여 실내에서 여가 시간을 보낼 때 부담이 가지 않는 운동을 할 수 있는 게임을 하며 음악을 들으면서 건강증진, 치매예방 및 심신을 안정시킬 수 있다. 본 시스템은 평균 77% 이상 동작인식성공률을 가진다.

• The Journal of the Acoustical Society of Korea
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• v.40 no.3
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• pp.228-233
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• 2021

In this paper, we propose a method to extract and classify features of multi-channel ElectroEncephalo Graphy (EEG) that change according to various musical tempo stimuli. In the proposed method, a 3D convolutional bidirectional gated recurrent neural network extracts spatio-temporal and long time-dependent features from the 3D EEG input representation transformed through the preprocessing. The experimental results show that the proposed tempo stimuli classification method is superior to the existing method and the possibility of constructing a music-based brain-computer interface.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.3
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• pp.237-245
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• 2020

Tropicália, which marked the beginning of Brazilian Popular Music (MPB), is a musical genre that originated in the Brazilian cultural movement and this arose in the late 1960s. The musicians who participated in this cultural movement delivered radical innovation by embracing the international music trend of the 60s, such as the Beatles phenomenon and the experimental and creative music of the electronic age. The music derived from this era incorporated the cannibalism of the Brazilian modernism cultural movement in the 1920s, and it presented a cultural consilience while maintaining the traditional Brazilian culture. In particular, the convergence of Tropicália with rock was especially prominent. As rock grew into the culture of resistance in both Britain and the United States, the convergent characteristic of Tropicália reflected the critical sociocultural view toward Brazil at that time. This paper focuses on the element of fusion of traditional Brazilian music and the rock music present in Tropicália. To present a case study, this paper selected music pieces by Caetano Veloso, the essential pillar of Tropicália, and examined each element through specific music analysis. This study aims to establish an academic foundation for third-world music and contributes to the development and creation of fusion music.

• Journal of Korea Multimedia Society
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• v.12 no.11
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• pp.1581-1592
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• 2009

Recently, we can hear various kinds of music in everywhere and anytime. If a user wants to find the music which was heard before in a street or cafe, but he does not know the title of the music, it is difficult to find it. That is the limitation of previous retrieval system of music data. To overcome these problems, we research a method of content-based retrieval of music data based on the recorded humming, the part of recorded music and the played musical instrument. In this paper, we investigated previous content-based retrieval methods of papers, systems and patents. Based on that, we research a method of content-based retrieval of music data. That is, in case of using the recorded humming and music for query, we extract the frequency information from the recorded humming/music and the stored music data by using FFT. We use a MIDI file in case of query by the played musical instrument. And by using dynamic programming matching, the error caused by the disparity of length between the input source with the stored music data could be reduced.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.3
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• pp.1-7
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• 2009

An approach for the classification of music genres using a Fuzzy c-Means(FcM) with divergence-based kernel is proposed and presented in this paper. The proposed model utilizes the mean and covariance information of feature vectors extracted from music data and modelled by Gaussian Probability Density Function (GPDF). Furthermore, since the classifier utilizes a kernel method that can convert a complicated nonlinear classification boundary to a simpler linear one, he classifier can improve its classification accuracy over conventional algorithms. Experiments and results on collected music data sets demonstrate hat the proposed classification scheme outperforms conventional algorithms including FcM and SOM 17.73%-21.84% on average in terms of classification accuracy.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.5
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• pp.102-108
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• 2011

Support vector machines are well known for their outstanding performance in pattern recognition fields. One example of their applications is music/speech classification for a standardized codec such as 3GPP2 selectable mode vocoder. In this paper, we propose a novel scheme that improves the speech/music classification of support vector machines based on the second-order conditional maximum a priori. While conventional support vector machine optimization techniques apply during training phase, the proposed technique can be adopted in classification phase. In this regard, the proposed approach can be developed and employed in parallel with conventional optimizations, resulting in synergistic boost in classification performance. According to experimental results, the proposed algorithm shows its compatibility and potential for improving the performance of support vector machines.