短时傅里叶变换
梯度下降
傅里叶变换
计算机科学
算法
窗口函数
信号处理
数学
语音识别
人工智能
数学优化
傅里叶分析
数字信号处理
数学分析
人工神经网络
电信
光谱密度
计算机硬件
作者
An Zhao,Krishna Subramani,Paris Smaragdis
标识
DOI:10.1109/icassp39728.2021.9413704
摘要
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.
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