Speaker-Independent Microphone Identification in Noisy Conditions
Antonio Giganti, Luca Cuccovillo, Paolo Bestagini, Patrick Aichroth, Stefano Tubaro · 2022 30th European Signal Processing Conference (EUSIPCO) · 2022
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.