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We now turn to the **Wavelet Packet 1-D** tool to analyze
a synthetic signal that is the sum of two linear chirps.

At the MATLAB command prompt, type

load sumlichr;

In the

**Wavelet Packets 1-D**tool, select**File**>**Import from Workspace**>**Import Signal**. When the**Import from Workspace**dialog box appears, select the`sumlichr`

variable. Click**OK**to import the data.The

`sumlichr`

signal is loaded into the**Wavelet Packet 1-D**tool.

Make the appropriate settings for the analysis. Select the

`db2`

wavelet, level`4`

, entropy`threshold`

, and for the threshold parameter type`1`

. Click the**Analyze**button.

The available entropy types are listed below.

Type | Description |
---|---|

Shannon | Nonnormalized entropy involving the logarithm of the squared value of each signal sample — or, more formally, $$-{\displaystyle \sum {s}_{i}^{2}\mathrm{log}({s}_{i}^{2})}.$$ |

Threshold | The number of samples for which the absolute value of the signal exceeds a threshold ε. |

Norm | The concentration in p. |

Log Energy | The logarithm of “energy,” defined as the sum over all samples: $$\sum \mathrm{log}({s}_{i}^{2})}.$$ |

SURE (Stein's Unbiased Risk Estimate) | A threshold-based method in which the threshold equals $$\sqrt{2{\mathrm{log}}_{e}\left(n{\mathrm{log}}_{2}(n)\right)}$$ where |

User | An entropy type criterion you define in a file. |

For more information about the available entropy types, user-defined
entropy, and threshold parameters, see the `wentropy`

reference
page and Choosing the Optimal Decomposition.

**Note**

Many capabilities are available using the command area on the
right of the **Wavelet Packet 1-D **window.

Because there are so many ways to reconstruct the original signal from the wavelet packet decomposition tree, we select the best tree before attempting to compress the signal.

Click the

**Best Tree**button.After a pause for computation, the

**Wavelet Packet 1-D**tool displays the best tree. Use the top and bottom sliders to spread nodes apart and pan over to particular areas of the tree, respectively.Observe that, for this analysis, the best tree and the initial tree are almost the same. One branch at the far right of the tree was eliminated.

Click the

**Compress**button.The

**Wavelet Packet 1-D Compression**window appears with an approximate threshold value automatically selected.The leftmost graph shows how the threshold (vertical black dotted line) has been chosen automatically (1.482) to balance the number of zeros in the compressed signal (blue curve that increases as the threshold increases) with the amount of energy retained in the compressed signal (purple curve that decreases as the threshold increases).

This threshold means that any signal element whose value is less than 1.482 will be set to zero when we perform the compression.

Threshold controls are located to the right (see the red box in the figure above). Note that the automatic threshold of 1.482 results in a retained energy of only 81.49%. This may cause unacceptable amounts of distortion, especially in the peak values of the oscillating signal. Depending on your design criteria, you may want to choose a threshold that retains more of the original signal's energy.

Adjust the threshold by typing

`0.8938`

in the text field opposite the threshold slider, and then press the**Enter**key.The value

`0.8938`

is a number that we have discovered through trial and error yields more satisfactory results for this analysis.After a pause, the

**Wavelet Packet 1-D Compression**window displays new information.Note that, as we have

*reduced*the threshold from 1.482 to 0.8938,The vertical black dotted line has shifted to the left.

The retained energy has

*increased*from 81.49% to 90.96%.The number of zeros (equivalent to the amount of compression) has

*decreased*from 81.55% to 75.28%.

Click the

**Compress**button.The

**Wavelet Packet 1-D**tool compresses the signal using the thresholding criterion we selected.The original (red) and compressed () signals are displayed superimposed. Visual inspection suggests the compression quality is quite good.

Looking more closely at the compressed signal, we can see that the number of zeros in the wavelet packets representation of the compressed signal is about 75.3%, and the retained energy about 91%.

If you try to compress the same signal using wavelets with exactly the same parameters, only 89% of the signal energy is retained, and only 59% of the wavelet coefficients set to zero. This illustrates the superiority of wavelet packets for performing compression, at least on certain signals.

You can demonstrate this to yourself by returning to the main **Wavelet Packet 1-D** window, computing the wavelet
tree, and then repeating the compression.

We now use the **Wavelet Packet 1-D** tool
to analyze a noisy chirp signal. This analysis illustrates the use
of Stein's Unbiased Estimate of Risk (SURE) as a principle for selecting
a threshold to be used for de-noising.

This technique calls for setting the threshold *T* to

$$T=\sqrt{2{\mathrm{log}}_{e}\left(n{\mathrm{log}}_{2}(n)\right)}$$

where *n* is the length of the signal.

A more thorough discussion of the SURE criterion appears in Choosing the Optimal Decomposition. For now, suffice it to
say that this method works well if your signal is normalized in such
a way that the data fit the model *x*(*t*)
= *f*(*t*) + *e*(*t*),
where *e*(*t*) is a Gaussian white
noise with zero mean and unit variance.

If you've already started the **Wavelet
Packet 1-D** tool and it is active on your computer's desktop, *skip
ahead to step 3*.

From the MATLAB prompt, type

`waveletAnalyzer`

.The

**Wavelet Analyzer**appears.Click the

**Wavelet Packet 1-D**menu item.The tool appears on the desktop.

**Importing a Signal**At the MATLAB command prompt, type

load noischir;

In the

**Wavelet Packet 1-D**tool, select**File**>**Import from Workspace**>**Import Signal**. When the**Import from Workspace**dialog box appears, select the`sumlichr`

variable. Click**OK**to import the data**Note**You can use

**File**>**Load**>**Signal**to load a signal by navigating to its location.The signal's length is 1024. This means we should set the SURE criterion threshold equal to

`sqrt(2.*log(1024.*log2(1024)))`

, or 4.2975.**Analyzing a Signal**Make the appropriate settings for the analysis. Select the

`db2`

wavelet, level`4`

, entropy type`sure`

, and threshold parameter 4.2975. Click the**Analyze**button.There is a pause while the wavelet packet analysis is computed.

**Note**Many capabilities are available using the command area on the right of the

**Wavelet Packet 1-D**window. Some of them are used in the sequel. For a more complete description, see Wavelet Packet Tool Features (1-D and 2-D).**Computing the Best Tree and Performing De-Noising**Computing the best tree makes the de-noising calculations more efficient.

Click the

**De-noise**button. This brings up the**Wavelet Packet 1-D De-Noising**window.Click the

**De-noise**button located at the center right side of the**Wavelet Packet 1-D De-Noising**window.

The results of the de-noising operation are quite good, as can be seen by looking at the thresholded coefficients. The frequency of the chirp signal increases quadratically over time, and the thresholded coefficients essentially capture the quadratic curve in the time-frequency plane.

You can also use the `wpdencmp`

function to perform
wavelet packet de-noising or compression from the command line.