• Title/Summary/Keyword: MCME

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Speech/Music Discrimination Using Mel-Cepstrum Modulation Energy (멜 켑스트럼 모듈레이션 에너지를 이용한 음성/음악 판별)

  • Kim, Bong-Wan;Choi, Dea-Lim;Lee, Yong-Ju
    • MALSORI
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    • no.64
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    • pp.89-103
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    • 2007
  • In this paper, we introduce mel-cepstrum modulation energy (MCME) for a feature to discriminate speech and music data. MCME is a mel-cepstrum domain extension of modulation energy (ME). MCME is extracted on the time trajectory of Mel-frequency cepstral coefficients, while ME is based on the spectrum. As cepstral coefficients are mutually uncorrelated, we expect the MCME to perform better than the ME. To find out the best modulation frequency for MCME, we perform experiments with 4 Hz to 20 Hz modulation frequency. To show effectiveness of the proposed feature, MCME, we compare the discrimination accuracy with the results obtained from the ME and the cepstral flux.

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Classification of Phornographic Videos Based on the Audio Information (오디오 신호에 기반한 음란 동영상 판별)

  • Kim, Bong-Wan;Choi, Dae-Lim;Lee, Yong-Ju
    • MALSORI
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    • no.63
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    • pp.139-151
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    • 2007
  • As the Internet becomes prevalent in our lives, harmful contents, such as phornographic videos, have been increasing on the Internet, which has become a very serious problem. To prevent such an event, there are many filtering systems mainly based on the keyword-or image-based methods. The main purpose of this paper is to devise a system that classifies pornographic videos based on the audio information. We use the mel-cepstrum modulation energy (MCME) which is a modulation energy calculated on the time trajectory of the mel-frequency cepstral coefficients (MFCC) as well as the MFCC as the feature vector. For the classifier, we use the well-known Gaussian mixture model (GMM). The experimental results showed that the proposed system effectively classified 98.3% of pornographic data and 99.8% of non-pornographic data. We expect the proposed method can be applied to the more accurate classification system which uses both video and audio information.

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Fast High-throughput Screening of the H1N1 Virus by Parallel Detection with Multi-channel Microchip Electrophoresis

  • Zhang, Peng;Park, Guenyoung;Kang, Seong Ho
    • Bulletin of the Korean Chemical Society
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    • v.35 no.4
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    • pp.1082-1086
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    • 2014
  • A multi-channel microchip electrophoresis (MCME) method with parallel laser-induced fluorescence (LIF) detection was developed for rapid screening of H1N1 virus. The hemagglutinin (HA) and nucleocapsid protein (NP) gene of H1N1 virus were amplified using polymerase chain reaction (PCR). The amplified PCR products of the H1N1 virus DNA (HA, 116 bp and NP, 195 bp) were simultaneously detected within 25 s in three parallel channels using an expanded laser beam and a charge-coupled device camera. The parallel separations were demonstrated using a sieving gel matrix of 0.3% poly(ethylene oxide) ($M_r$ = 8,000,000) in $1{\times}$ TBE buffer (pH 8.4) with a programmed step electric field strength (PSEFS). The method was ~20 times faster than conventional slab gel electrophoresis, without any loss of resolving power or reproducibility. The proposed MCME/PSEFS assay technique provides a simple and accurate method for fast high-throughput screening of infectious virus DNA molecules under 400 bp.

Classification of Phornographic Videos Using Audio Information (오디오 신호를 이용한 음란 동영상 판별)

  • Kim, Bong-Wan;Choi, Dae-Lim;Bang, Man-Won;Lee, Yong-Ju
    • Proceedings of the KSPS conference
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    • 2007.05a
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    • pp.207-210
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    • 2007
  • As the Internet is prevalent in our life, harmful contents have been increasing on the Internet, which has become a very serious problem. Among them, pornographic video is harmful as poison to our children. To prevent such an event, there are many filtering systems which are based on the keyword based methods or image based methods. The main purpose of this paper is to devise a system that classifies the pornographic videos based on the audio information. We use Mel-Cepstrum Modulation Energy (MCME) which is modulation energy calculated on the time trajectory of the Mel-Frequency cepstral coefficients (MFCC) and MFCC as the feature vector and Gaussian Mixture Model (GMM) as the classifier. With the experiments, the proposed system classified the 97.5% of pornographic data and 99.5% of non-pornographic data. We expect the proposed method can be used as a component of the more accurate classification system which uses video information and audio information simultaneously.

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