论文标题
通过梯度下降优化短时傅立叶变换参数
Optimizing Short-Time Fourier Transform Parameters via Gradient Descent
论文作者
论文摘要
短期傅立叶变换（STFT）一直是信号处理的主要内容，通常是许多音频任务的第一步。使用STFT时非常熟悉的过程是寻找最佳的STFT参数，因为如果选择不佳，它们通常会产生重大副作用。这些参数通常是根据整数数量的样本来定义的，这使得它们的优化非平凡。在本文中，我们展示了一种方法，该方法使我们能够获得有关任意成本功能的STFT参数的梯度，从而使能够采用梯度下降优化数量（例如STFT窗口长度或STFT HOP大小）的能力。我们这样做是为了在整个输入过程中保持恒定的参数值，并且对于这些参数必须随时间变化以适应不同信号特性的情况。
The Short-Time Fourier Transform (STFT) has been a staple of signal processing, often being the first step for many audio tasks. A very familiar process when using the STFT is the search for the best STFT parameters, as they often have significant side effects if chosen poorly. These parameters are often defined in terms of an integer number of samples, which makes their optimization non-trivial. In this paper we show an approach that allows us to obtain a gradient for STFT parameters with respect to arbitrary cost functions, and thus enable the ability to employ gradient descent optimization of quantities like the STFT window length, or the STFT hop size. We do so for parameter values that stay constant throughout an input, but also for cases where these parameters have to dynamically change over time to accommodate varying signal characteristics.
