Speaker-Independent Microphone Identification in Noisy Conditions

Speaker-Independent Microphone Identification in Noisy Conditions

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...

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Veröffentlicht in:arXiv.org 2023-01
Hauptverfasser: Giganti, Antonio, Cuccovillo, Luca, Bestagini, Paolo, Aichroth, Patrick, Tubaro, Stefano
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Computer Science - Sound
Neural networks
Noise reduction
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creator Giganti, Antonio
Cuccovillo, Luca
Bestagini, Paolo
Aichroth, Patrick
Tubaro, Stefano
description 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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subjects Computer Science - Sound
Neural networks
Noise reduction
title Speaker-Independent Microphone Identification in Noisy Conditions
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