• Journal of Fashion Business
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• v.4 no.3
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• pp.79-90
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• 2000

On the beginning of 21C, in which it is a facing problem that the expansive image of future in fashion should be proposed from the comprehensive analysis for the fashion trend. Therefore, in this study, the trends of capricious fashion are distinctly quantified by investigating the cue of fashion in each styles. Also, the systematic evaluation is carried out of analyzing photographs to which the four important fashion styles. In particular, this study takes the practical and numerical results through quantitative analysis by statistical treatment as well as through qualitative analysis that has been formerly used in the other studies. The purposes of this study are to examine fashion trends expressed in important styles in the 1990s, and to formulate productive fashion of the future. In the qualitative analysis, the four important fashions of neo-mods/jazz, neo-hippie/grunge, sportive-casual and techos/cyber-punk are grouped. In the quantitative analysis, statistical data are sampled from Collection II of the 1990s A/W. It takes frequency, percentage, $x^2$-test and etc, by using the comprehensive tools for statistical treatment. There were significant differences between the A/W fashion. According to the cues, there are also significant differences between the fashion in the 1990s. The results of this study are summarized as follows: (1) In 'Neo-Mos/Jazz' style shows highly androgynous look, deep and strong tone, green/blue colors, natural fabric, stripe pattern, long hair style, and hided make-up. (2) 'Neo-hippie/gnenge' style shows highly folklore look, vivid tone purple colors, seethrough/knit fabric, natural /traditional pattern, decorative hair special make-up. (3) 'Sportive casuals' style shows highly sportive look, greish tone, white/grey colours, natural fabric, solid patten, bobbed hair, and natural make-up. (4) 'Techno/cyber punk style shows highly comocorps look, pale tone black colors avangard fabric, solid patten, punk/dyed hair special make-up.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.12
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• pp.691-698
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• 2014

An L-band single side band(SSB) mixer with CMOS switches and a C-band quadrature voltage-controlled oscillator(QVCO) have been developed using the TowerJazz 0.18-um RFCMOS process. The SSB mixer exhibits a conversion gain of 6.6 ~ 7.5 dB with a 70-dBc image rejection ratio and 65-dBc port isolation. The oscillation frequency range of the QVCO is 6.2 ~ 6.7 GHz with an output power of 4~6 dBm. For measurement, 1.8 V supply voltage is used while drawing 36 mA for the mixer and 23 mA for the QVCO.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.6
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• pp.271-278
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• 2004

In this paper, we propose a content-based audio genre classification algorithm that automatically classifies the query audio into five genres such as Classic, Hiphop, Jazz, Rock, Speech using digital sign processing approach. From the 20 seconds query audio file, the audio signal is segmented into 23ms frame with non-overlapped hamming window and 54 dimensional feature vectors, including Spectral Centroid, Rolloff, Flux, LPC, MFCC, is extracted from each query audio. For the classification algorithm, k-NN, Gaussian, GMM classifier is used. In order to choose optimum features from the 54 dimension feature vectors, SFS(Sequential Forward Selection) method is applied to draw 10 dimension optimum features and these are used for the genre classification algorithm. From the experimental result, we can verify the superior performance of the proposed method that provides near 90% success rate for the genre classification which means 10%∼20% improvements over the previous methods. For the case of actual user system environment, feature vector is extracted from the random interval of the query audio and it shows overall 80% success rate except extreme cases of beginning and ending portion of the query audio file.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.4
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• pp.136-141
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• 2011

In this paper, we propose an approach to improve the data detection performance of the acoustic data transmission system based on the modulated complex lapped transform (MCLT). We first present an effective analysis of the features and the detection method of data in the acoustic data transmission system. And then feature vectors which are applied to the Gaussian mixture model (GMM) are selected from relevant parameters of the previous system for the efficient data detection. For the purpose of evaluating the performance of the proposed algorithm, Bit error rate (BER) of the received data was measured at different environments (music genres (rock, pop, classic, jazz) and different distances (1m∼5m) from the loudspeaker to the microphone in a office room) and yields better results compared with the conventional scheme of the acoustic data transmission system based on the MCLT.

• The Journal of the Acoustical Society of Korea
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• v.23 no.7
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• pp.532-539
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• 2004

In this Paper, we propose a content-based music information retrieval (MIR) system base on the query-by-example (QBE) method. The proposed system is implemented to retrieve queried music from a dataset where 60 music samples were collected for each of the four genres in Classical, Hiphop. Jazz. and Reck. resulting in 240 music files in database. From each query music signal, the system extracts 60 dimensional feature vectors including spectral centroid. rolloff. flux base on STFT and also the LPC. MFCC and Beat information. and retrieves queried music from a trained database set using Euclidean distance measure. In order to choose optimum features from the 60 dimension feature vectors, SFS method is applied to draw 10 dimension optimum features and these are used for the Proposed system. From the experimental result. we can verify the superior performance of the proposed system that provides success rate of 84% in Hit Rate and 0.63 in MRR which means near 10% improvements over the previous methods. Additional experiments regarding system Performance to random query Patterns (or portions) and query lengths have been investigated and a serious instability problem of system Performance is Pointed out.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.5
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• pp.949-958
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• 2018

A voice recognition control system is a system for controlling a peripheral device by recognizing a voice. Recently, a voice recognition control system have been applied not only to smart devices but also to various environments ranging from IoT(: Internet of Things), robots, and vehicles. In such a voice recognition control system, the recognition rate is lowered due to the ambient noise in addition to the voice of the user. In this paper, we propose a dual channel acoustic beamforming hardware architecture for MEMS(: Microelectromechanical Systems) microphones to eliminate ambient noise in addition to user's voice. And the proposed hardware architecture is designed as ASIC(: Application-Specific Integrated Circuit) using TowerJazz $0.18{\mu}m$ CMOS(: Complementary Metal-Oxide Semiconductor) technology. The designed dual channel acoustic beamforming ASIC has a die size of $48mm^2$, and the directivity index of the user's voice were measured to be 4.233㏈.

• Journal of the Korean Society of Clothing and Textiles
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• v.21 no.3
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• pp.544-558
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• 1997

Black street style has made unique fashion in popular music such as Jazz, Soul, Rhythm Il Blues. Reggae, and Rap, and it is counterculture and subculture against white. Furthermore, the black street style has played a starring role in the development of white culture as well as black culture, which emerged in direct opposition to the dominant cultures practised by a fraction of fellow countrymen within the black diaspora. The objectives of this study are to examine the social chronology of the black street style and the contemporary fashion, and the influences of the black street style on white culture. The seeds of black's style were sown in the late forties, developing throughout the fifties with the arrival of black immigrants from the west Indies and its examples were zooties, hip cats 8l hipsters, modernists. Rude boy & two-tone was anti·fashion style in sixties and then rastafarians continued in seventies costume is used to convey an essential symbolic class and ethnic message. The latest black's street fashion is hip-hop dress, which is pluralistic and electric, and funk is also erratic. During its ten-year reign as an international style, it has undergone numerous shifts because it is decline of racism B-boy & flygirls toraggamuffins & bhangra style to acid Jazz. These have played a crucial part in influencing the gigh fashion and avant-grade fashion designers' work. Today's street fashion has characteristics of postmodern culture without a racism in global village. Moreover, pop music stars take an effect on the street style continuously. With the opening of a new century, the study of the street style will overcast popular fashion and suggest the direction of fashion design.

• The Journal of the Korea Contents Association
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• v.22 no.6
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• pp.273-282
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• 2022

The narrative of the film, Sweet Smell of Success has a multi-layered structure. In Clifford Odets's scenario work, numerous lines for the main plot and subplots repeated a cycle of creation, decomposition, deletion, and modification to enhance the density of the story. As a result, whenever an actor spoke a line or paused, an action or an event that would trigger another line was created, giving depth and persuasiveness to the character's performance. The music of Sweet Smell of Success has multi-layered elements. The non-diegetic music was covered by the orchestral pieces performed by Elmer Bernstein's big band orchestra and the jazz pieces performed by Fred Katz's combo band. The diegetic music was mostly covered by the jazz pieces performed by the Chico Hamilton Quintet. The practical task of the film music was to reinforce or supplement the effect of the narrative driver, and the additional task was to realize the estrangement effect and the aesthetics of stagnation. The possibility and significance of intertextuality of subplots and double sound of this film are not simply confined to the limits of the film noir genre.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.175-182
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• 2022

In our daily life, we come across different types of information, for example in the format of multimedia and text. We all need different types of information for our common routines as watching/reading the news, listening to the radio, and watching different types of videos. However, sometimes we could run into problems when a certain type of information is required. For example, someone is listening to the radio and wants to listen to jazz, and unfortunately, all the radio channels play pop music mixed with advertisements. The listener gets stuck with pop music and gives up searching for jazz. So, the above example can be solved with an automatic audio classification system. Deep Learning (DL) models could make human life easy by using audio classifications, but it is expensive and difficult to deploy such models at edge devices like nano BLE sense raspberry pi, because these models require huge computational power like graphics processing unit (G.P.U), to solve the problem, we proposed DL model. In our proposed work, we had gone for a low complexity model for Audio Event Detection (AED), we extracted Mel-spectrograms of dimension 128×431×1 from audio signals and applied normalization. A total of 3 data augmentation methods were applied as follows: frequency masking, time masking, and mixup. In addition, we designed Convolutional Neural Network (CNN) with spatial dropout, batch normalization, and separable 2D inspired by VGGnet [1]. In addition, we reduced the model size by using model quantization of float16 to the trained model. Experiments were conducted on the updated dataset provided by the Detection and Classification of Acoustic Events and Scenes (DCASE) 2020 challenge. We confirm that our model achieved a val_loss of 0.33 and an accuracy of 90.34% within the 132.50KB model size.

• Proceedings of the IEEK Conference
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• 2004.08c
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• pp.768-771
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• 2004

In this paper, we propose a content-based audio genre classification algorithm that automatically classifies the query audio into five genres such as Classic, Hiphop, Jazz, Rock, Speech using digital signal processing approach. From the 20 second query audio file, 54 dimensional feature vectors, including Spectral Centroid, Rolloff, Flux, LPC, MFCC, is extracted from each query audio. For the classification algorithm, k-NN, Gaussian, GMM classifier is used. In order to choose optimum features from the 54 dimension feature vectors, SFS (Sequential Forward Selection) method is applied to draw 10 dimension optimum features and these are used for the genre classification algorithm. From the experimental result, we verify the superior performance of the SFS method that provides near $90{\%}$ success rate for the genre classification which means $10{\%}$-$20{\%}$ improvements over the previous methods