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