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

Abstract

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.

Identification of Attack Traffic Using Machine Learning in Smart IoT Networks

“…(i) In paper [1], the Hybrid Monotone Empirical Mode Decomposition (HM-EMD) is a recent EMD-based method of generating intrinsic mode functions (IMFs) using the monotone property. e monotone property assumes that, at each IMF extraction step, local maxima and minima are either increasing or decreasing.…”