论文标题
使用卷积神经网络和改进袋的声学色情识别
Acoustic Pornography Recognition Using Convolutional Neural Networks and Bag of Refinements
论文作者
论文摘要
互联网上公开可用的大量色情音频严重威胁了儿童的身心健康，但是很少被检测和过滤这些音频。在本文中，我们首先提出了一个基于声学色情识别的基于卷积神经网络（CNN）模型。然后，我们研究了一系列改进，并通过消融研究来验证其有效性。最后，我们将所有细化堆叠在一起，以验证它们是否可以进一步提高模型的准确性。我们新收集的大型数据集的实验结果包括224127色情音频和274206正常样本，证明了我们提出的模型和这些改进的有效性。具体而言，所提出的模型的准确性为92.46％，当所有细化均组合时，精度将进一步提高到97.19％。
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.
