• Journal of Audiology & Otology
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• v.25 no.3
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• pp.119-123
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• 2021

Background and Objectives: The influence of music training on different areas of the brain has been extensively researched, but the underlying neurobehavioral mechanisms remain unknown. In the present study, the effects of training for more than three years in Carnatic music (an Indian form of music) on the discrimination ability of different areas of the brain were tested using P300 analysis at three electrode placement sites. Subjects and Methods: A total of 27 individuals, including 13 singers aged 16-30 years (mean±standard deviation, 23±3.2 years) and 14 non-singers aged 16-30 years (mean age, 24±2.9 years), participated in this study. The singers had 3-5 years of formal training experience in Carnatic music. Cortical activities in areas corresponding to attention, discrimination, and memory were tested using P300 analysis, and the tests were performed using the Intelligent Hearing System. Results: The mean P300 amplitude of the singers at the Fz electrode placement site (5.64±1.81) was significantly higher than that of the non-singers (3.85±1.60; t(25)=3.3, p<0.05). The amplitude at the Cz electrode placement site in singers (5.90±2.18) was significantly higher than that in non-singers (3.46±1.40; t(25)=3.3, p<0.05). The amplitude at the Pz electrode placement site in singers (4.94±1.89) was significantly higher than that in non-singers (3.57±1.50; t(25)=3.3, p<0.05). Among singers, the mean P300 amplitude was significantly higher in the Cz site than the other placement sites, and among non-singers, the mean P300 amplitude was significantly higher in the Fz site than the other placement sites, i.e., music training facilitated enhancement of the P300 amplitude at the Cz site. Conclusions: The findings of this study suggest that more than three years of training in Carnatic singing can enhance neural coding to discriminate subtle differences, leading to enhanced discrimination abilities of the brain, mainly in the generation site corresponding to Cz electrode placement.

• Journal of the Korea Convergence Society
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• v.10 no.9
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• pp.23-29
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• 2019

In this paper, the mood of images was classified into eight categories through a deep convolutional neural network and video was automatically generated using proper background music. Based on the collected image data, the classification model is learned using a multilayer perceptron (MLP). Using the MLP, a video is generated by using multi-class classification to predict image mood to be used for video generation, and by matching pre-classified music. As a result of 10-fold cross-validation and result of experiments on actual images, each 72.4% of accuracy and 64% of confusion matrix accuracy was achieved. In the case of misclassification, by classifying video into a similar mood, it was confirmed that the music from the video had no great mismatch with images.

• Journal of Korea Multimedia Society
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• v.23 no.4
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• pp.615-624
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• 2020

By enabling musicians to freely control the elements involved in sound production and tone generation with a variety of timbre, synthesizers have revolutionized and permanently changed music since the 1960s. Paik-Abe Video Synthesizer, a masterpiece of video art maestro Nam June Paik, is a prominent example of re-interpretation of this new musical instrument in the realm of video and audio. This article examines Paik-Abe Video Synthesizer as an innovative instrument to play videos from the perspective of audiovisual art, and establishes its aesthetic value and significance through both artistic and technical analysis. The instrument, which embodied the concept of image sampling and real-time interactive video as an image-based multi-channel music production tool, contributed to establishing a new relationship between sound and image within the realm of audiovisual art. The fact that his video synthesizer not only adds image to sound, but also presents a complete fusion of image and sound as an image instrument with musical characteristics, becomes highly meaningful in this age of synesthesia.

• Journal of information and communication convergence engineering
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• v.2 no.4
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• pp.233-236
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• 2004

This paper shows the easy way to composite a music for non-musicians. Namely, only select a chord and put it to special program which called "the chord composition program", and then the composition program creates the rest chords. After doing this process, non-musicians overwrite their own melodies in accordance with created chords. Finally, they can perform their own music with the sound of master-keyboard and other sound sources by using the band-in-a-box program.x program.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2019.06a
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• pp.173-175
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• 2019

Animated images formats such as WebP are highly portable graphics formats that are being used everywhere on the Internet. Despite their small sizes and duration, WebP image previews the video without watching the entire content with minimum bandwidth. This paper proposed a novel method to generate personalized WebP images in the client side using its computation resources. The proposed system automatically extracts the WebP image from climax point using music analysis. Based on user interest, the system predicts the genre using Convolutional Neural Network (CNN). The proposed method can easily integrate with streaming platforms such as YouTube, Netflix, Hulu, and others.

• Journal of Positioning, Navigation, and Timing
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• v.11 no.2
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• pp.91-98
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• 2022

In the next generation wireless communication system, the beamforming technique based on a massive antenna is one of core technologies for transmitting and receiving huge amounts of data, efficiently and accurately. For highly performed and highly reliable beamforming, it is required to accurately estimate the Angle of Arrival (AOA) for the desired signal incident to an antenna. Employing the massive antenna with a large number of elements, although the accuracy of the AOA estimation is enhanced, its computational complexity is dramatically increased so much that real-time communication is difficult. In order to improve this problem, AOA estimation algorithms based on the massive antenna with the low computational complexity have been actively studied. In this paper, we compute and analyze the computational complexity of the cascade AOA estimation algorithm based on the Flexible Massive Concentric Circular Array (FMCCA). In addition, its computational complexity is compared to conventional AOA estimation techniques such as the Multiple Signal Classification (MUSIC) algorithm with the high resolution and the Only Beamspace MUSIC (OBM) algorithm.

• Journal of Korea Multimedia Society
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• v.18 no.1
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• pp.52-64
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• 2015

Recently, as the quality of life has been improving, search items in the area of entertainment represent an increasing share of the total usage of Internet portal sites. Information retrieval in the entertainment area is mainly depending on keywords that users are inputting, and the results of information retrieval are the contents that contain those keywords. In this paper, we propose a search method that takes natural language inputs and retrieves the database pertaining to entertainment. The main components of our study are the simple Korean morphological analyzer using case particle information, predicate-oriented token generation, standardized pattern generation coherent to tokens, and automatic generation of the corresponding SQL queries. We also propose an efficient retrieval system that searches the most relevant results from the database in terms of natural language querying, especially in the restricted domain of music, and shows the effectiveness of our system.

• The Research Journal of the Costume Culture
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• v.18 no.6
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• pp.1090-1111
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• 2010

Thanks to both developments in various media such as the internet and TV and China's economic growth, the fashion market in China has gain a lot of attention by global businesses as a newly-raised spending market. Fashion companies in Korea have entered into the china since the late 1990s. For them to get competitive edges, they have to differentiate their brand by creating new design based on the culture becoming mega-trend in fashion market. So in this research, I try to create fashion designs based on Hallyu Stars' fashion styles and images, who come on as a new culture code in China and other Asian. For this, I conducted theological consideration on what the Hallyu is, and looked into fashion styles in soft dramas which 5 Hallyu Stars started in and street fashion in Shanghai in China. Based on the outcomes of analysis, I figured out those star's fashion style and created products targeting young generation born after 1980s in China. The conclusion of this study is as follows. Firstly, the very definition of Hallyu which can be described as a phenomenon in which Korean movies, soap operas, and pop music have become immensely popular throughout Asia.has been expanding to signify the proliferation of Korean culture as a whole. Secondly, having selected the 5 female stars representing Hallyu, we were able to analyze and categorize their fashion styles on the basis of their music videos, movies, and soap-operas. Thirdly, In order to explore the level of influence of Hallyu fashion industry, we studied the street fashion of Shanghai The result was that we could observe both the slim and feminine cool casual style and the cute and affectionate pretty casual style simultaneously.

• Journal of the Korea Society of Computer and Information
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• v.20 no.5
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• pp.53-63
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• 2015

Singing voice synthesis is the generation of a song using a computer given its lyrics and musical notes. Hidden Markov models (HMM) have been proved to be the models of choice for text to speech synthesis. HMMs have also been used for singing voice synthesis research, however, a huge database is needed for the training of HMMs for singing voice synthesis. And commercially available singing voice synthesis systems which use the piano roll music notation, needs to adopt the easy to read standard music notation which make it suitable for singing learning applications. To overcome this problem, we use a speech database for training context dependent HMMs, to be used for singing voice synthesis. Pitch and duration control methods have been devised to modify the parameters of the HMMs trained on speech, to be used as the synthesis units for the singing voice. This work describes a singing voice synthesis system which uses a MusicXML based music score editor as the front-end interface for entry of the notes and lyrics to be synthesized and a hidden Markov model based text to speech synthesis system as the back-end synthesizer. A perceptual test shows the feasibility of our proposed system.

• Smart Media Journal
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• v.11 no.10
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• pp.65-75
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• 2022

Deep learning is used as a creative tool that could overcome the limitations of existing analysis models and generate various types of results such as text, image, and music. In this paper, we propose a method necessary to preprocess audio data using the Niko's MIDI Pack sound source file as a data set and to generate music using Bi-LSTM. Based on the generated root note, the hidden layers are composed of multi-layers to create a new note suitable for the musical composition, and an attention mechanism is applied to the output gate of the decoder to apply the weight of the factors that affect the data input from the encoder. Setting variables such as loss function and optimization method are applied as parameters for improving the LSTM model. The proposed model is a multi-channel Bi-LSTM with attention that applies notes pitch generated from separating treble clef and bass clef, length of notes, rests, length of rests, and chords to improve the efficiency and prediction of MIDI deep learning process. The results of the learning generate a sound that matches the development of music scale distinct from noise, and we are aiming to contribute to generating a harmonistic stable music.