用Gabor滤波器替代SincNet，提升语音伪造检测性能。
Audio Spoof Detection with GaborNet
- 用Gabor滤波器构建特征提取层，替代传统Sinc函数。
- 在RawNet2和RawGAT-ST上测试，显著提升伪造音频检测准确率。
- 适合语音安全、反欺骗系统研究者参考。
音频特征提取的新方向是直接在时域处理原始采样信号，该方法在神经网络时代尤为有效，如SincNet中使用 sinc 函数作为卷积核。由于 sinc 函数有限长度，导致频域出现伪影，类似信号加窗效应。近期新方法改用 Gabor 滤波器替代 sinc 函数，因结果复杂，需配合平方模或高斯低通池化等改进。本文系统评估了基于 Gabor 滤波器组的 GaborNet 及其变体，在主流音频伪造检测架构 RawNet2 与 RawGAT-ST 中的表现。同时研究了通过编解码转换、房间响应及加性噪声进行音频增强的有效性。
原文摘要 · Abstract (English)
An direction of development in the extraction of features from audio signals is based on processing raw samples in the time domain. Such an approach appears to be effective, especially in the era of neural networks. An example is SincNet. In this solution, the core of the neural network layer is a set of sinc functions that are convolved with the input signal. Due to the finite length of sinc functions, distortions appear in the frequency domain of the convolved signal, the same as in the case of windowing the signal. Recently, a new approach has been developed that uses Gabor filters to replace sinc functions. Due to the complex results, further modifications had to be applied, such as squared modulus or Gaussian Lowpass Pooling. In this work, an ingestion layer based on a bank of Gabor filters, named GaborNet, and its modifications are intensively examined within the popular RawNet2 and RawGAT- ST architectures. These have been developed for the purpose of audio spoof detection. Another issue that has been investigated was audio augmentation using codec conversions, room responses, and additive noises.
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