• Proceedings of the Korea Contents Association Conference
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• 2003.05a
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• pp.393-398
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• 2003

Video which contains the multiple data such as text, images, audio and motion of objects is typical multimedia data. Multimedia retrieval system using XML is essential for efficient rep. of multimedia data. Therefore, multimedia retrieval system for retrieval and structural understanding is needed to retrieve the multimedia data. This Paper Proposes the multimedia retrieval system based on XML Meta-data modeling techniques.

• Proceedings of the Korea Database Society Conference
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• 1996.04a
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• pp.3-29
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• 1996

Multimedia Management -Naive View -images, audio, and video -store these as BLOBs, along with a tag, in an RDB for read retrieval(omitted)

• Journal of the Korea Society of Computer and Information
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• v.13 no.3
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• pp.99-106
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• 2008

Recently, digital music retrieval is using in many fields (Web portal. audio service site etc). In existing fields, Meta data of music are used for digital music retrieval. If Meta data are not right or do not exist, it is hard to get high accurate retrieval result. Contents based information retrieval that use music itself are researched for solving upper problem. In this paper, we propose Same music recognition method using similarity measurement. Feature data of digital music are extracted from waveform of music using Simplified MFCC (Mel Frequency Cepstral Coefficient). Similarity between digital music files are measured using DTW (Dynamic time Warping) that are used in Vision and Speech recognition fields. We success all of 500 times experiment in randomly collected 1000 songs from same genre for preying of proposed same music recognition method. 500 digital music were made by mixing different compressing codec and bit-rate from 60 digital audios. We ploved that similarity measurement using DTW can recognize same music.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.419-421
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• 2006

The importance of automatic discrimination image data has evolved as a research topic over recent years. We have used forward neural network as a classifier using sound data features within image data, our initial tests have shown encouraging results that indicate the viability of our approach.

• 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.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.1075-1080
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• 2009

이 글은 Music Information Retrieval(MIR) 기법을 사용하여 오디오 콘텐츠의 리듬 정보를 자동으로 분석하고 이를 시각화하는 방법에 대해 다룬다. 특히 MIR을 활용한 간단한 시각화(sound visualization) 어플리케이션을 소개함으로써 음악 정보 분석이 디자인, 시각 예술에서 다양하게 활용될 수 있음을 보이고자 한다. 음악적 정보를 시각 예술로 담아내려는 시도는 20세기 초 아방가르드 화가들에 의해 본격적으로 시작되었다. 80년대 이후에는 컴퓨터 기술의 급속한 발전으로 사운드와 이미지를 디지털 영역에서 쉽게 하나로 다룰 수 있게 되었고, 이에 따라 다양한 오디오 비주얼 예술작품들이 등장하였다. MIR은 오디오 콘텐츠로부터 음악적 정보를 분석하는 DSP(Digital Signal Processing) 기술로 최근 디지털 콘텐츠 시장의 확장과 더불어 연구가 활발히 진행되고 있다. 특히 웹이나 모바일에서는 이미 다양한 상용 어플리케이션이 적용되고 있는데 query-by-humming과 같은 음악 인식 어플리케이션이 대표적인 경우이다. 이 글에서는 onset 검출(onset detection)을 중심으로 음악적 리듬을 분석하는 알고리즘을 살펴보고 기본적인 조형원리에 따라 이를 시각화하는 어플리케이션의 예를 소개한다.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.21 no.4
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• pp.67-80
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• 2010

The output and usage rate of audio-visual materials have sharply increased as the information industry advances and diverse archives became available. However, the awareness of the audio-visual archives are more of a separate record with collateral value. The organizations that hold these materials have very weak system of the various areas such as the categories and archiving methods. Moreover, the management system varies among the organizations, so the users face difficulty retrieving and utilizing the audio-visual materials. Thus, this study examined the feasibility of the synchronized management of audio-visual archives by comparing the descriptive elements of the audio-visual archives in internal key agencies. The study thereby examines the feasibility of the metadata element of the organizations and that of synchronized management to propose the effect of the use of management, retrieval and service of efficient AV materials. The study also proposes the improvement of descriptive element of metadata.

• Proceedings of the Korea Database Society Conference
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• 1997.10a
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• pp.24-48
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• 1997

. Supported byㆍDARPA′s image Understanding (IU) program under "Video Retrieval Based on Language and image Analysis" project.DARPA′s Computer Assisted Education and Training Initiative program (CAETI)ㆍObjective: Develop practical systems for automatic understanding and indexing of video sequences using both audio and video tracks(omitted)

• Proceedings of the KSPS conference
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• 2006.11a
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• pp.71-74
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• 2006

Many works have been done in the field of retrieving audio segments that contain human speeches without captions. To retrieve newly coined words and proper nouns, subwords were commonly used as indexing units in conjunction with query or document expansion. Among them, document expansion with subwords has serious drawback of large computation overhead. Therefore, in this paper, we propose Expected Matching Score based document expansion that effectively reduces computational overhead without much loss in retrieval precisions. Experiments have shown 13.9 times of speed up at the loss of 0.2% in the retrieval precision.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.2
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• pp.445-451
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• 2010

This paper presents a threshold adaptation based voice query transcription scheme for music information retrieval. The proposed scheme analyzes monophonic voice signal and generates its transcription for diverse music retrieval applications. For accurate transcription, we propose several advanced features including (i) Energetic Feature eXtractor (EFX) for onset, peak, and transient area detection; (ii) Modified Windowed Average Energy (MWAE) for defining multiple small but coherent windows with local threshold values as offset detector; and finally (iii) Circular Average Magnitude Difference Function (CAMDF) for accurate acquisition of fundamental frequency (F0) of each frame. In order to evaluate the performance of our proposed scheme, we implemented a prototype music transcription system called AMT2 (Automatic Music Transcriber version 2) and carried out various experiments. In the experiment, we used QBSH corpus [1], adapted in MIREX 2006 contest data set. Experimental result shows that our proposed scheme can improve the transcription performance.