Identification of Audio Processing Operations Based on Convolutional Neural Network

TLDR: The experimental results show that the proposed convolutional neural network to detect audio processing operations can significantly outperform related methods based on hand-crafted features and other CNN architectures, and can achieve state-of-the-art results for both binary and multiple classification.

Abstract: To reduce the tampering artifacts and/or enhance audio quality, some audio processing operations are often applied in the resulting tampered audio. Like image forensics, the detection of various post processing operations has become very important for audio authentication. In this paper, we propose a convolutional neural network (CNN) to detect audio processing operations. In the proposed method, we carefully design the network architecture, with particular attention to the frequency representation for the audio input, the activation function and the depth of the network. In our experiments, we evaluate the proposed method on audio clips with 12 commonly used audio processing operations and of three different small sizes. The experimental results show that our method can significantly outperform related methods based on hand-crafted features and other CNN architectures, and can achieve state-of-the-art results for both binary and multiple classification.