Name
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. |
Year | Venue | DocType |
|---|---|---|
2022 | European Signal Processing Conference (EUSIPCO) | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Antonio Giganti | 1 | 0 | 0.34 |
| Luca Cuccovillo | 2 | 20 | 2.54 |
| Paolo Bestagini | 3 | 261 | 32.01 |
| Patrick Aichroth | 4 | 1 | 1.09 |
| Stefano Tubaro | 5 | 1033 | 119.50 |