Perform and interpret time-frequency analysis of signals using the continuous wavelet transform.

Perform and interpret basic signal multiresolution analysis (MRA).

この例では、連続ウェーブレット変換 (CWT) を使用して変調信号を解析する方法を示します。

Use the ウェーブレット信号デノイザー app to denoise a real-valued 1-D signal. You can create and compare multiple versions of a denoised signal with the app and export the desired denoised signal to your MATLAB® workspace. To reproduce the denoised signal in your workspace, or to apply the same denoising parameters to other data, you can generate and edit a MATLAB script. This example illustrates one possible workflow.

深層学習と連続ウェーブレット変換を使用して、心拍心電図のデータを分類します。

There are a number of different variations of the wavelet transform. This example focuses on the maximal overlap discrete wavelet transform (MODWT). The MODWT is an undecimated wavelet transform over dyadic (powers of two) scales, which is frequently used with financial data. One nice feature of the MODWT for time series analysis is that it partitions the data variance by scale. To illustrate this, consider the quarterly chain-weighted U.S. real GDP data for 1974Q1 to 2012Q4. The data were transformed by first taking the natural logarithm and then calculating the year-over-year difference. Obtain the MODWT of the real GDP data down to level six with the 'db2' wavelet. Examine the variance of the data and compare that to the variances by scale obtained with the MODWT.