Automatic extraction of pornographic contents using radon transform based audio features

This paper focuses on the problem of classifying pornographic sounds, such as sexual scream or moan, to detect and block the objectionable multimedia contents. To represent the large temporal variations of pornographic sounds, we propose a novel feature extraction method based on Radon transform. Radon transform provides a way to extract the global trend of orientations in a 2-D region and therefore it is applicable to the time-frequency spectrograms in the long-range segment to capture the large temporal variations of sexual sounds. Radon feature is extracted using histograms and flux of Radon coefficients. We adopt Gaussian mixture model to statistically represent the pornographic and non-pornographic sounds, and the test sounds are classified by using likelihood ratio test. Evaluations on several hundred pornographic and non-pornographic sound clips indicate that the proposed features can achieve satisfactory results that this approach could be used as an alternative to the image-based methods.