• 한국HCI학회:학술대회논문집
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• 한국HCI학회 2009년도 학술대회
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• pp.522-525
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• 2009

본 논문에서는 인터넷의 역기능으로 현대 사회에 큰 문제를 야기 시키는 음란성 유해 동영상을 내용기반으로 판별하기 위한 방법을 제안하였다. 유해 동영상에서 오디오 데이터를 이용하여 특징을 추출하였다. 사용된 오디오 특징은 주파수 스펙트럼, 자기상관, MFCC이다. 음란성의 내용이 될 수 있는 소리의 특징을 추출하였고 동영상 전체 오디오에서 해당 소리의 특징과 일치하는지를 측정하여 유해성을 판별하였다. 제안한 방법의 실험은 각 특징마다 유해 판별 측정 결과와 다중 특징을 이용한 측정 결과를 비교 수행하였다. 하나의 오디오 특징만을 추출하여 사용하였을 때 보다 다중 특징의 사용이 좋은 결과를 얻을 수 있었다.

• 대한음성학회지:말소리
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• 제63호
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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.

• 대한음성학회:학술대회논문집
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• 대한음성학회 2007년도 한국음성과학회 공동학술대회 발표논문집
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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.