Investigating Feature Reduction Strategies for Replay Antispoofing in Voice Biometrics

Mankad, Sapan H.; Garg, Sanjay; Patel, Megh; Adalja, Harshil

doi:10.1007/978-3-030-34872-4_44

Cited by 3 publications

(1 citation statement)

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“…e background sound will also change during the replay spoofing. erefore, the construction of features representing the hidden information in audio scenes can be used to detect the fake audio [12].…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction Method for Hidden Information in Audio Streams Based on HM-EMD

Lou

Zuo

et al. 2021

Security and Communication Networks

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Using fake audio to spoof the audio devices in the Internet of Things has become an important problem in modern network security. Aiming at the problem of lack of robust features in fake audio detection, an audio streams’ hidden feature extraction method based on a heuristic mask for empirical mode decomposition (HM-EMD) is proposed in this paper. First, using HM-EMD, each signal is decomposed into several monotonic intrinsic mode functions (IMFs). Then, on the basis of IMFs, basic features and hidden information features HCFs of audio streams are constructed, respectively. Finally, a machine learning method is used to classify audio streams based on these features. The experimental results show that hidden information features of audio streams based on HM-EMD can effectively supplement the nonlinear and nonstationary information that traditional features such as mel cepstrum features cannot express and can better realize the representation of hidden acoustic events, which provide a new research idea for fake audio detection.

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Section: Introductionmentioning

confidence: 99%