Exploiting Frequency Response for the Identification of Microphone Using Artificial Neural Networks

Hafeez A, Malik KM, Malik H. Exploiting Frequency Response for the Identification of Microphone Using Artificial Neural Networks. In: AES Conference: 2019 AES International Conference on Audio Forensics. Audio Engineering Society; 2019. Paper 19. Available from: https://aes.org/publications/elibrary-page/?id=20470

@inproceedings{Hafeez2019_20470,
  author = {Hafeez, Azeem and Malik, Khalid Mahmood and Malik, Hafiz},
  title = {{Exploiting Frequency Response for the Identification of Microphone Using Artificial Neural Networks}},
  booktitle = {AES Conference: 2019 AES International Conference on Audio Forensics},
  note = {Paper 19},
  year = {2019},
  month = jun,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=20470}
}

TY  - CPAPER
TI  - Exploiting Frequency Response for the Identification of Microphone Using Artificial Neural Networks
AU  - Hafeez, Azeem
AU  - Malik, Khalid Mahmood
AU  - Malik, Hafiz
T2  - AES Conference: 2019 AES International Conference on Audio Forensics
M1  - Paper 19
PY  - 2019
DA  - 2019/06/06
UR  - https://aes.org/publications/elibrary-page/?id=20470
PB  - Audio Engineering Society
LA  - en
AB  - Microphone identi?cation addresses the challenge of identifying the microphone signature from the recorded signal. An audio recording system (consisting of microphone, A/D converter, codec, etc.) leaves its unique traces in the recorded signal. Microphone system can be modeled as a linear time invariant system. The impulse response of this system is convoluted with the audio signal which is recorded using “the” microphone. This paper makes an attempt to identify "the" microphone from the frequency response of the microphone. To estimate the frequency response of a microphone, we employ sine sweep method which is independent of speech characteristics. Sinusoidal signals of increasing frequencies are generated, and subsequently we record the audio of each frequency. Detailed evaluation of sine sweep method shows that the frequency response of each microphone is stable. A neural network based classi?er is trained to identify the microphone from recorded signal. Results show that the proposed method achieves microphone identi?cation having 100% accuracy.
ER  -