The spoof speech detection (SSD) is the essential countermeasure for
automatic speaker verification systems. Although SSD with magnitude features in
the frequency domain has shown promising results, the phase information also
can be important to capture the artefacts of certain types of spoofing attacks.
Thus, both magnitude and phase features must be considered to ensure the
generalization ability to diverse types of spoofing attacks. In this paper, we
investigate the failure reason of feature-level fusion of the previous works
through the entropy analysis from which we found that the randomness difference
between magnitude and phase features is large, which can interrupt the
feature-level fusion via backend neural network; thus, we propose a phase
network to reduce that difference. Our SSD system: phase network equipped
Res2Net achieved significant performance improvement, specifically in the
spoofing attack for which the phase information is considered to be important.
Also, we demonstrate our SSD system in both known- and unknown-kind SSD
scenarios for practical applications.

本文针对音频反欺骗技术进行研究，通过熵分析发现频率相位特征与幅度特征的随机度存在巨大差异，导致以往的特征级融合存在失败情况。为此，提出使用相位网络，降低上述差异，并对其进行了实证研究，结果表明相位网络能有效提高系统的性能表现，并在该领域的已知及未知应用场景中得到了应用。

基于 Res2Net 和相位网络的相位感知伪造语音检测

Phase-Aware Spoof Speech Detection Based on Res2Net with Phase Network

Music source separation with deep neural networks typically relies only on
amplitude features. In this paper we show that additional phase features can
improve the separation performance. Using the theoretical relationship between
STFT phase and amplitude, we conjecture that derivatives of the phase are a
good feature representation opposed to the raw phase. We verify this conjecture
experimentally and propose a new DNN architecture which combines amplitude and
phase. This joint approach achieves a better signal-to distortion ratio on the
DSD100 dataset for all instruments compared to a network that uses only
amplitude features. Especially, the bass instrument benefits from the phase
information.

本文探讨了利用深度神经网络在音乐源分离中仅依赖幅度特征与在加入相位特征后分离性能的提升，提出了一种新的神经网络结构，该结构结合了幅度和相位，实验结果表明，相比仅使用幅度特征的网络，在 DSD100 测试集上使用相位衍生特征能够有效提升音频信号的信噪比，特别地，低音乐器的分离效果因此得到了明显的提升。