A recording device identification algorithm based on improved PNCC feature and two-step discriminative training

作者：He, Qian-Hua; Wang, Zhi-Feng^*; Rudnicky, Alexander I; Zhu, Zheng-Yu; Li, Xin-Chao

来源：Acta Electronica Sinica, 2014, 42(1): 191-198.

DOI：10.3969/j.issn.0372-2112.2014.01.031

• Digital audio
• Discriminative training
• GMM-UBM
• PNCC
• Recording devices

Recording device identification is a kind of blind digital audio forensic technique, which extracts digital evidence of device mechanism involved in the generation of the speech recording by analyzing the acoustic signal. This paper proposes a recording device identification algorithm which is based on improved PNCC feature and two-step discriminative training. Due to the fact that silence periods contain the device information and is not affected by speaker and texture factors, this paper extracts improved PNCC from silence periods, which uses long term analysis to remove the effect of background noise. GMM-UBM is set as the baseline system, which is improved by two steps discriminative training. The experimental result indicates that the average accuracy of recording device identification on 30 devices is 90.23%;for 15 inset and 15 outset devices testing, the EER is 15.17% and ACC is 96.65%, which proves the effectiveness of the proposed algorithm.

• 出版日期2014-1
• 单位华南理工大学