论文标题
相对于窗口长度的可区分短时傅立叶变换
A differentiable short-time Fourier transform with respect to the window length
论文作者
论文摘要
在本文中，我们通过使窗口长度成为可通过梯度下降来优化的连续参数，而不是经验调谐的整数价值高音参数来优化神经网络中的频谱图。此时，该贡献主要是理论上的，但是将修改后的STFT插入任何现有的神经网络都很简单。在本地箱中心固定并且独立于窗口长度参数的情况下，我们首先定义了STFT的可区分版本。然后，我们讨论窗口长度影响垃圾箱的位置和数量的更困难的情况。我们说明了该新工具在估计和分类问题上的好处，这表明它不仅对神经网络也可能引起任何基于STFT的信号处理算法的关注。
In this paper, we revisit the use of spectrograms in neural networks, by making the window length a continuous parameter optimizable by gradient descent instead of an empirically tuned integer-valued hyperparameter. The contribution is mostly theoretical at this point, but plugging the modified STFT into any existing neural network is straightforward. We first define a differentiable version of the STFT in the case where local bins centers are fixed and independent of the window length parameter. We then discuss the more difficult case where the window length affects the position and number of bins. We illustrate the benefits of this new tool on an estimation and a classification problems, showing it can be of interest not only to neural networks but to any STFT-based signal processing algorithm.
