论文标题
在嘈杂条件下，独立于扬声器的麦克风识别
Speaker-Independent Microphone Identification in Noisy Conditions
论文作者
论文摘要
这项工作提出了一种从语音记录中识别的源设备识别方法，该方法应用了基于神经网络的denoising，以减轻使用噪声注入的反法差攻击的影响。通过比较DeNosing对麦克风分类的三个最新特征的影响来评估该方法，从而在应用或不使用deNosing的情况下确定其歧视功率。所提出的框架为嘈杂的材料实现了显着的性能，更普遍地验证了在设备识别噪声录音之前施加DeNosing的有用性。
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.
