时频分析
小波
算法
信号(编程语言)
傅里叶变换
小波变换
理论(学习稳定性)
信号处理
计算机科学
数学
功能(生物学)
语音识别
人工智能
数学分析
计算机视觉
数字信号处理
计算机硬件
程序设计语言
机器学习
滤波器(信号处理)
生物
进化生物学
摘要
Two different procedures for effecting a frequency analysis of a time-dependent signal locally in time are studied. The first procedure is the short-time or windowed Fourier transform; the second is the wavelet transform, in which high-frequency components are studied with sharper time resolution than low-frequency components. The similarities and the differences between these two methods are discussed. For both schemes a detailed study is made of the reconstruction method and its stability as a function of the chosen time-frequency density. Finally, the notion of time-frequency localization is made precise, within this framework, by two localization theorems.< >
科研通智能强力驱动
Strongly Powered by AbleSci AI