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A large number of pornographic audios publicly available on the Internetseriously threaten the mental and physical health of children, but these audiosare rarely detected and filtered. In this paper, we firstly propose aconvolutional neural networks (CNN) based model for acoustic pornographyrecognition. Then, we research a collection of refinements and verify theireffectiveness through ablation studies. Finally, we stack all refinementstogether to verify whether they can further improve the accuracy of the model.Experimental results on our newly-collected large dataset consisting of 224127pornographic audios and 274206 normal samples demonstrate the effectiveness ofour proposed model and these refinements. Specifically, the proposed modelachieves an accuracy of 92.46% and the accuracy is further improved to 97.19%when all refinements are combined.

Acoustic Pornography Recognition Using Convolutional Neural Networks and Bag of Refinements