RARS: Recognition of Audio Recording Source Based on Residual Neural Network

With the popularity of mobile devices and the emergence of various audio-editing tools, it becomes easier to produce and forge audio files. Many criminals will forge false audio information as evidence. Therefore, audio forensics technology becomes particularly important. Audio recording device identification technology, which can verify the authenticity and uniqueness of the evidence obtained, is one of the promising branches of audio forensics technology. In this article, a novel neural-network-based framework using the device noise feature is proposed to identify the source of recording according to the device traces generated by the device during the recording. We also propose a new neural network model RARS (Recognition of Audio Recording Source based on residual neural network). The proposed framework achieves state-of-the-art performance on MOBIPHONE, the only publicly available dataset in this field. Moreover, we build a new dataset based on the latest mobile phones and tablet devices. Our method achieves good performance on both the two datasets, which proves that our model has a certain degree of reusability and robustness.