  This work proposes a method for source device identification from speech
recordings that applies neural-network-based denoising, to mitigate the impact
of counter-forensics attacks using noise injection. The method is evaluated by
comparing the impact of denoising on three state-of-the-art features for
microphone classification, determining their discriminating power with and
without denoising being applied. The proposed framework achieves a significant
performance increase for noisy material, and more generally, validates the
usefulness of applying denoising prior to device identification for noisy
recordings.
