• Proceedings of the Acoustical Society of Korea Conference
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• autumn
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• pp.503-504
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• 2004

휴대용 개인 단말기기(Personal digital assistants)의 높은 보급률에 따라 초소형 스피커(Micro-loudspeaker)의 수요가 급격히 증가하면서, 초소형 스피커의 성능 개선이 절실히 요구되고 있다. 본 연구에서는 초소형 스피커의 진동판 소재로 사용되는 폴리에틸렌 나프탈레이트(PEN)와 폴리에테르 이미르(PEI)의 수지 계열 진동판과 니켈(Ni) 진동판의 방사 효율을 비교 분석하여, 진동계의 관점에서 금속 진동판의 활용 가능성에 대해 고찰하였다.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.210-212
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• 2005

The personal video recorder is a consumer electronics device that records television shows to a hard disk in digital format. In this paper, we propose an implementation method of personal video recorder for digital TV. The proposed personal video recorder includes cpu and system control modules, graphics and display module, audio DSP module, digital I/O module, NIM module, graphic software library, and embedded software modules for providing a lot of PVR functions such as live or reserved recordings, browsing of recorded content list, trick play and time shifting. Especially, combining trick play with time shifting makes much more convenient functions such as pausing live TV, instant replay of interesting scenes, and skipping advertising.

• Journal of KIISE
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• v.43 no.11
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• pp.1281-1297
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• 2016

In recent years, personal videos have seen a tremendous growth due to the substantial increase in the use of smart devices and networking services in which users create and share video content easily without many restrictions. However, taking both into account would significantly improve event detection performance because videos generally have multiple modalities and the frame data in video varies at different time points. This paper proposes an event detection method. In this method, high-level features are first extracted from multiple modalities in the videos, and the features are rearranged according to time sequence. Then the association of the modalities is learned by means of DNN to produce a personal video event detector. In our proposed method, audio and image data are first synchronized and then extracted. Then, the result is input into GoogLeNet as well as Multi-Layer Perceptron (MLP) to extract high-level features. The results are then re-arranged in time sequence, and every video is processed to extract one feature each for training by means of DNN.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2013.10a
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• pp.720-721
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• 2013

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.18 no.7
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• pp.701-710
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• 2008

Including a TV set and a monitor, personal audio system is raising a great interest. In this study, we applied a method to make a good bright zone around the user and dark zone to other region by maximizing the ratio of sound energy between the bright and dark zone. It has been well known as acoustic contrast control. We have attempted to use a line loudspeaker array system to localize the sound in our listening zone. It depends on the size of the zone and array parameters, for example, array size, loudspeaker unit spacing, wave length of sound. We have considered these parameters as spatial variables and studied the effects. And we have found that each spatial variable has its own characteristic and shows very different effect. Genetic algorithms are introduced to find out the optimum value of spatial variables. As a result, we can improve the result of the acoustic contrast control by optimum value of spatial variables.

• The KIPS Transactions:PartD
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• v.15D no.2
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• pp.257-262
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• 2008

This paper describes the implementations of the application service based on embedded Linux; Personal Audio Recorder (PAR) which uses WiBro network for data communications and CDMA network for voice communications. At PAR, when PAR client starts voice recording on a dual-mode terminal, the CDMA voice data of caller and callee is transmitted to storage server located in the Internet through WiBro network. Then, PAR server stores voice data on storage server according to the call number and call time. In case of shortage of storage space on terminal, PAR makes user to store voice data. And, PAR can search a catalog of stored data on server and play the specific content.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.6
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• pp.1135-1142
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• 2017

In this paper, we propose an algorithm that can automatically mix the screen and sound by tracking the change of the sound data according to the screen change so that the real sound can be implemented in the personal broadcasting service. Since the personal broadcasting service is often broadcasted lively, it should be convenient to have a real-time mixing. Through experiments, it was confirmed that the sound pressure changes in a wide range in the high frequency band related to the clarity for understanding according to the rotation angle change of the screen. Regression analysis of the sound pressure changes at 2kHz, 4kHz, and 8kHz, The attenuation change of sound pressure was observed at the slope of -1.17, the slope of -2.0, and the slope of -2.44 for each frequency. Therefore, these experiment results can be applied to the VR service. This study is expected to be useful data in the implementation of personal broadcasting service.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.166-175
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• 2022

Digital transformation is driving so many changes in daily life and industry. The automobile industry is in a similar situation. In some cases, element techniques in areas called metabuses are also being adopted, such as 3D animated digital cockpit, around view, and voice AI, etc. Through the growth of the mobile market, the norm of human-computer interaction (HCI) has been evolving from keyboard-mouse interaction to touch screen. The core area was the graphical user interface (GUI), and recently, the audio user interface (AUI) has partially replaced the GUI. Since it is easy to access and intuitive to the user, it is quickly becoming a common area of the in-vehicle experience (IVE), especially. The benefits of a AUI are freeing the driver's eyes and hands, using fewer screens, lower interaction costs, more emotional and personal, effective for people with low vision. Nevertheless, when and where to apply a GUI or AUI are actually different approaches because some information is easier to process as we see it. In other cases, there is potential that AUI is more suitable. This is a study on a proposal to actively apply a AUI in the near future based on the context of various scenes occurring to improve IVE.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1689-1701
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• 2019

In recent years, personal videos have been shared online due to the popular uses of portable devices, such as smartphones and action cameras. A recent report predicted that 80% of the Internet traffic will be video content by the year 2021. Several studies have been conducted on the detection of main video events to manage a large scale of videos. These studies show fairly good performance in certain genres. However, the methods used in previous studies have difficulty in detecting events of personal video. This is because the characteristics and genres of personal videos vary widely. In a research, we found that adding a dataset with the right perspective in the study improved performance. It has also been shown that performance improves depending on how you extract keyframes from the video. we selected frame segments that can represent video considering the characteristics of this personal video. In each frame segment, object, location, food and audio features were extracted, and representative vectors were generated through a CNN-based recurrent model and a fusion module. The proposed method showed mAP 78.4% performance through experiments using LSVC data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.284-286
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• 2021

With the continuous development of the Internet and digital, platforms are emerging to store large amounts of media data and provide customized services to individuals through online. Companies that provide these services recommend movies that suit their personal tastes to promote media consumption. Each company is doing a lot of research on various algorithms to recommend media that users prefer. Movies are divided into genres such as action, melodrama, horror, and drama, and the film's audio (music, sound effect, voice) is an important production element that makes up the film. In this research, based on movie trailers, we extract audio for each genre, check the commonalities of audio for each genre, distinguish movie genres through supervised learning of artificial intelligence, and propose a utilization method for generating metadata in the future.