• ETRI Journal
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• v.43 no.6
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• pp.1081-1092
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• 2021

Electroencephalography (EEG) recordings taken during the perception of music tempo contain information that estimates the tempo of a music piece. If information about this tempo stimulus in EEG recordings can be extracted and classified, it can be effectively used to construct a music-based brain-computer interface. This study proposes a novel convolutional recurrent attention model (CRAM) to extract and classify features corresponding to tempo stimuli from EEG recordings of listeners who listened with concentration to the tempo of musics. The proposed CRAM is composed of six modules, namely, network inputs, two-dimensional convolutional bidirectional gated recurrent unit-based sample encoder, sample-level intuitive attention, segment encoder, segment-level intuitive attention, and softmax layer, to effectively model spatiotemporal features and improve the classification accuracy of tempo stimuli. To evaluate the proposed method's performance, we conducted experiments on two benchmark datasets. The proposed method achieves promising results, outperforming recent methods.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.1
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• pp.89-94
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• 2021

In this paper, we presented the results of analysis with data obtained through EEG measurements to confirm the effect of musical stimulus when performing mathematical tasks. While the subject was solving a mathematical task, favorite and unfavorite music classified according to the subject's preference were presented as musical stimulus and the tasks were divided into memorization task and procedure task. The data measured in the EEG experiments was divided into theta waves, SMR waves and mid-beta waves which are the frequency bands related to concentration to compare the relative power spectrum values. In our results, in the case of comparing no music with favorite music and no music with unfavorite music, a significant difference was observed in the several channels, and the average difference was shown in the channels F3 and F4 of the frontal lobe. In that channels, the power was found to be greater when the music was presented than the case where there was no music. Depending on the subject's preference, it was confirmed that favorite music showed greater brain activity than unfavorite music.

• The Journal of the Convergence on Culture Technology
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• v.5 no.4
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• pp.131-136
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• 2019

Since the term technopop was first used 40 years ago, techno music has evolved with technological advances. However, few studies have examined techno music from a historical point of view. In particular, it is necessary to explore the techno music in relation to the development of industrial technology. Therefore, this paper aimed to examine the historical context of techno music from the early days of Detroit techno as well as the industrial significance of techno culture in Europe. We also provide suggestions regarding the further development of techno music.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.19 no.3
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• pp.294-303
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• 2008

ISAR(Inverse Synthetic Aperture Radar) images represent the 2-D(two-dimensional) spatial distribution of RCS (Radar Cross Section) of an object, and they can be applied to the problem of target identification. A traditional approach to ISAR imaging is to use a 2-D IFFT(Inverse Fast Fourier Transform). However, the 2-D IFFT results in low resolution ISAR images especially when the measured frequency bandwidth and angular region are limited. In order to improve the resolution capability of the Fourier transform, various high-resolution spectral estimation approaches have been applied to obtain ISAR images, such as AR(Auto Regressive), MUSIC(Multiple Signal Classification) or Modified MUSIC algorithms. In this study, these high-resolution spectral estimators as well as 2-D IFFT approach are combined with a recently developed ISAR image classification algorithm, and their performances are carefully analyzed and compared in the framework of radar target recognition.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.377-379
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• 2021

According to a 2006 paper by a music therapist and a music therapist in elementary schools, the demand for special education increased, and a 2018 music education study showed that music rooms and equipment increased, but it was difficult to move or lacked various instruments. In this work, we develop a module that combines hardware and software for social improvement education in autistic children using tools. Various instrument sounds can be set using piezo sensors and Arduino, so you can experience various instruments through simple operation and there are instruments designed for music therapy through modularity. Hopefully, the study will help disabled children heal their music.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.4
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• pp.773-778
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• 2020

In this paper, we analyzed the effects of musical stimuli on humans in performing mathematical tasks through EEG measurements. The musical stimuli were divided into preferred music and non-preferred music, and mathematical tasks were divided into memorization task and procedure task. The data measured in the EEG experiments was divided into frequency bands of Theta, SMR, and Mid-beta because of the concentration. In our results, preferred music causes more positive emotional response than no music and non-preferred music regardless of the type of mathematical task.

• The Journal of the Acoustical Society of Korea
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• v.41 no.2
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• pp.115-121
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• 2022

In this paper, we propose a music classification system according to user emotions using Electroencephalogram (EEG) features that appear when listening to music. In the proposed system, the relationship between the emotional EEG features extracted from EEG signals and the auditory features extracted from music signals is learned through a deep regression neural network. The proposed system based on the regression model automatically generates EEG features mapped to the auditory characteristics of the input music, and automatically classifies music by applying these features to an attention-based deep neural network. The experimental results suggest the music classification accuracy of the proposed automatic music classification framework.

• Journal of Advanced Navigation Technology
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• v.20 no.3
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• pp.258-264
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• 2016

In this paper, we propose a computationally efficient direction of arrival (DoA) estimation algorithm based on propagator method with non-uniform array. While the co-prime array techniques can improve the resolution of DoA, they generally lead to high computational complexity as the length of the coarray aperture. To reduce the complexity we use the propagator method that does not require singular value decomposition (SVD). Through simulations, we compare MUSIC with uniform lineary array, propagator method with uniform linear array, MUSIC with co-prime array, and the proposed scheme and observe that the performance of the proposed scheme is significantly better than MUSIC or propagator method with uniform linear array while it is slightly worse than computationally much more expensive co-prime array MUSIC scheme.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.7
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• pp.801-806
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• 2015

Recently, retro music is popular in the domestic music industry. Even in the movie industry, remake or retro movies are made continuously. In this paper, we consider the reason of remake in the viewpoint of mathematics and show that there is a maximum number of lyrics, pictures, music, and movies that human can make due to the characteristics of digital contents. The time that human cannot make new lyrics, new pictures, new music, and new movies any more will be estimated. Therefore, artists will have difficulties to make new contents in the future, and human have to listen and see the previous lyrics, movies, music, or pictures in the future. This study will give a deep philosophical insight for contents industry.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.8
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• pp.868-876
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• 2016

MUSIC (multiple signal classification) algorithm is a representative algorithm estimating the angle of arrival using the eigenvalues and eigenvectors. Generally, the eigenvalues and eigenvectors are obtained through the eigen-analysis, but this analysis requires high computational complexity and late convergence time. For this reason, it is almost impossible to construct the real-time system with low-cost using this approach. Even though QR iteration is considered as the eigen-analysis approach to improve these problems, this is inappropriate to apply to the MUSIC algorithm. In this paper, we analyze the problems of conventional method based on the eigenvalues for convergence decision and propose the improved decision algorithm using the eigenvectors.