Skip to content Skip to navigation Skip to collection information

OpenStax-CNX

You are here: Home » Content » Analysis of Speech Signal Spectrums Using the L2 Norm » Signal Extraction (Analysis of Speech Signal Spectrums Using the L2 Norm)

Navigation

Recently Viewed

This feature requires Javascript to be enabled.
 

Signal Extraction (Analysis of Speech Signal Spectrums Using the L2 Norm)

Module by: Nicholas. E-mail the author

Summary: This section describes how the signal is extracted.

V – Signal Extraction

Prior to signal comparison, the signals must first be aligned. The first step to alignment is to extract the relevant signal from the entire data segment. To perform this initial processing we smooth the absolute value of the data, and find the maximum of the smoothed data. Given the index of the maximum, we extend the bounds of our hypothesized signal outwards until the amount of energy within our bounds exceeds a threshold percentage. Energy is defined as the L2 norm of the data signal, shown in (5.1). This procedure is encapsulated in the function “extractSignal”.

Table 1
E = i data ( i ) 2 E = i data ( i ) 2 size 12{E= Sum cSub { size 8{i} } {"data" \( i \) rSup { size 8{2} } } } {} (5.1)

The threshold percentage was determined empirically. Figure 5.1 shows examples of the signal that was extracted for different threshold percentages. Portions of the data that were edited out are replaced with 0. As one can see, thresholding at 90% removes relevant information from the data, and thresholding at 99% retains much of the irrelevant values in the data. We set the energy threshold to 95%.

Figure 1
Figure 1 (graphics1.png)

Figure 5.1: Results of identifying the signal in the data segment based on different energy percentage thresholds. The original data shown is of Nicholas stating the word “one”. Portions that are removed from the signal are set to 0.

Collection Navigation

Content actions

Download:

Collection as:

PDF | EPUB (?)

What is an EPUB file?

EPUB is an electronic book format that can be read on a variety of mobile devices.

Downloading to a reading device

For detailed instructions on how to download this content's EPUB to your specific device, click the "(?)" link.

| More downloads ...

Module as:

PDF | More downloads ...

Add:

Collection to:

My Favorites (?)

'My Favorites' is a special kind of lens which you can use to bookmark modules and collections. 'My Favorites' can only be seen by you, and collections saved in 'My Favorites' can remember the last module you were on. You need an account to use 'My Favorites'.

| A lens I own (?)

Definition of a lens

Lenses

A lens is a custom view of the content in the repository. You can think of it as a fancy kind of list that will let you see content through the eyes of organizations and people you trust.

What is in a lens?

Lens makers point to materials (modules and collections), creating a guide that includes their own comments and descriptive tags about the content.

Who can create a lens?

Any individual member, a community, or a respected organization.

What are tags? tag icon

Tags are descriptors added by lens makers to help label content, attaching a vocabulary that is meaningful in the context of the lens.

| External bookmarks

Module to:

My Favorites (?)

'My Favorites' is a special kind of lens which you can use to bookmark modules and collections. 'My Favorites' can only be seen by you, and collections saved in 'My Favorites' can remember the last module you were on. You need an account to use 'My Favorites'.

| A lens I own (?)

Definition of a lens

Lenses

A lens is a custom view of the content in the repository. You can think of it as a fancy kind of list that will let you see content through the eyes of organizations and people you trust.

What is in a lens?

Lens makers point to materials (modules and collections), creating a guide that includes their own comments and descriptive tags about the content.

Who can create a lens?

Any individual member, a community, or a respected organization.

What are tags? tag icon

Tags are descriptors added by lens makers to help label content, attaching a vocabulary that is meaningful in the context of the lens.

| External bookmarks