短时傅里叶变换
可微函数
傅里叶变换
时频表示法
梯度下降
时频分析
窗口(计算)
计算机科学
窗口函数
离散时间傅里叶变换
瞬态(计算机编程)
算法
帧(网络)
数学
傅里叶分析
人工智能
数学分析
计算机视觉
人工神经网络
电信
操作系统
滤波器(信号处理)
作者
Maxime Leiber,Yosra Marnissi,Axel Barrau,Mohamed El Badaoui
标识
DOI:10.1109/icassp49357.2023.10095245
摘要
This paper presents a gradient-based method for on-the-fly optimization for both per-frame and per-frequency window length of the short-time Fourier transform (STFT), related to previous work in which we developed a differentiable version of STFT by making the window length a continuous parameter. The resulting differentiable adaptive STFT possesses commendable properties, such as the ability to adapt in the same time-frequency representation to both transient and stationary components, while being easily optimized by gradient descent. We validate the performance of our method in vibration analysis.
科研通智能强力驱动
Strongly Powered by AbleSci AI