Digital Recording System Identification Based on Blind Deconvolution

In this work, we develop a theoretical framework for reliable digital recording system identification from digital audio files alone, for forensic purposes. A digital recording system consists of a microphone and a digital sound processing card. We view the cascade as a system of unknown transfer function. We expect the same manufacturer and model microphone-sound card combinations to have very similar/near identical transfer functions, bar any unique manufacturing defect. Input voice (or other) signals are modeled as non-stationary processes. The technical problem under consideration becomes blind deconvolution with non-stationary inputs, as it manifests itself in the specific application of digital audio recording equipment classification. Experimental results demonstrate over 99.2% accuracy in identification of the recording devices.