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arXiv · 2206.11640

Speaker-Independent Microphone Identification in Noisy Conditions

Antonio Giganti·Luca Cuccovillo·Paolo Bestagini·Patrick Aichroth·Stefano Tubaro
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Abstract

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.

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Antonio Giganti, Luca Cuccovillo, Paolo Bestagini, Patrick Aichroth, Stefano Tubaro. 2022-06-23. Speaker-Independent Microphone Identification in Noisy Conditions. https://doi.org/10.23919/eusipco55093.2022.9909800

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