Perform and interpret continuous wavelet analysis. The example helps you answer common questions such as: What is the difference between continuous and discrete wavelet analysis? Why are the frequencies, or scales in continuous wavelet analysis logarithmically spaced? In what types of signal analysis problems are continuous wavelet techniques particularly useful?

执行和解释基本信号多解析分析（MRA）。该示例使用模拟和真实数据来回答以下问题：多分析分析是什么意思？关于我的信号有什么见解，我可以获得多解析分析？不同MRA技术的优势和缺点是什么？这里提供的许多分析可以使用信号多解像アナライザーアナライザー应用程序复制。

使用连续小波变换（CWT）分析调制信号。

Use the Wavelet Signal Denoiser 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。

使用小波时间散射和音频数据存储对音乐摘录进行分类。

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.