Skip to content Skip to navigation Skip to collection information

OpenStax-CNX

You are here: Home » Content » ECE 301 Projects Fall 2003 » Methods

Navigation

Table of Contents

Lenses

What is a lens?

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.

This content is ...

Affiliated with (What does "Affiliated with" mean?)

This content is either by members of the organizations listed or about topics related to the organizations listed. Click each link to see a list of all content affiliated with the organization.
  • Rice University ELEC 301 Projects

    This collection is included inLens: Rice University ELEC 301 Project Lens
    By: Rice University ELEC 301

    Click the "Rice University ELEC 301 Projects" link to see all content affiliated with them.

  • Rice Digital Scholarship

    This collection is included in aLens by: Digital Scholarship at Rice University

    Click the "Rice Digital Scholarship" link to see all content affiliated with them.

Also in these lenses

  • Lens for Engineering

    This module and collection are included inLens: Lens for Engineering
    By: Sidney Burrus

    Click the "Lens for Engineering" link to see all content selected in this lens.

  • EW Public Lens display tagshide tags

    This collection is included inLens: Ed Woodward's Public Lens
    By: Ed Woodward

    Comments:

    "assafdf"

    Click the "EW Public Lens" link to see all content selected in this lens.

    Click the tag icon tag icon to display tags associated with this content.

Recently Viewed

This feature requires Javascript to be enabled.

Tags

(What is a tag?)

These tags come from the endorsement, affiliation, and other lenses that include this content.
 

Methods

Module by: Pranav Chitkara, Mark Yeh, Chris Forbis. E-mail the authors

Summary: Explain our goal and approach towards the project.

Goal

Analyze an input speech sample and return the vowels that are present.

Approach

Vowels are highly periodic, so they have distinctive Fourier representations. That is, there are large values at a particular frequency, in this case the lower end of the spectrum. By using Fourier analysis on an input signal, we will be able to detect via matched filters the input vowel sound.

Initially, we decided to build a database of the five fundamental vowel sounds. We used the MATLAB program. A project member recorded a voice sample of each vowel several times, ran the samples through the auto-regressive filter, and then calculated the first two formant frequencies from the frequency response of the vowel. Each voice sample was recorded at 8 kHz, and 256-sample windows were input into the auto-regressive model. The purpose of the auto-regressive model on each window was to get the transfer function of the vocal tract and output the frequency response of each voice sample. After the database was built, the next step was to record several samples of words or phrases and input them into the filter. To filter out the consonants, our program checked the magnitude values of the frequency response of each window. Normally, consonants will have significantly lower magnitudes than vowel sounds, and our program utilized a threshold to filter out only consonants. Next, we used a type of match filter to determine which vowel sound the sample corresponded to. We did this by setting up a series of five flags in our program, one for each vowel. At first, when each window came through, all the flags were set to true. The program then began comparing the known formant frequencies of each vowel to the voice sample. If the sample did not pass a threshold of a known vowel formant frequency, then the flag of that vowel was set to false. If there were multiple flags set true when comparing the first formant frequency of the voice sample, then the program then moved on to compare the second formants. After each 256 window was processed, we used a smoother to eliminate anomalies (due to unclear pronunciation, noise in the sample, etc.) and then output each vowel. Our final code used to detect vowels.

Figure 1: Flowchart of Approach
Figure 1 (flowchart.gif)

Auto Regressive Model

In our project, the only data for the vocal tract that we have is the windowed sound chunk that was produced at a particular time. Assuming a standard impulse input, the autoregressive model will take this chunk and compute a model for the vocal tract at the particular moment the sound was uttered. The vocal tract can be modeled simply as a series of linked cylindrical tubes, with the formants appearing due to the transition between these different tubes. Since the autoregressive model for this model of the vocal tract produces an all-pole transfer function (because we only have the output), ideally we should notice peaks at all of the particular resonant frequencies. These peaks do appear, and they are our formants.

Hamming Window

Our windowing method that we used was a hamming window; you can see a very similar window, the hanning window, in the images below. The hamming window looks roughly like one period of a sine wave, as opposed to a rectangular window. This tapering at the ends is needed because otherwise you get anomalous behavior in the frequency domain. A hamming or hanning window provides a truer representation of the frequency content of the signal.

Figure 2: The top waveform is a segment 1024 samples long taken from the beginning of the "Rice University" phrase. Computing figure 1 involved creating frames, here demarked by the vertical lines, that were 256 samples long and finding the spectrum of each. If a rectangular window is applied (corresponding to extracting a frame from the signal), oscillations appear in the spectrum (middle of bottom row). Applying a Hanning window gracefully tapers the signal toward frame edges, thereby yielding a more accurate computation of the signal's spectrum at that moment of time. (From Spectrograms)
Figure 2 (hanning1.png)
Figure 3: In comparison with the original speech segment shown in the upper plot, the non-overlapped Hanning windowed version shown below it is very ragged. Clearly, spectral information extracted from the bottom plot could well miss important features present in the original. (From Spectrograms)
Figure 3 (hanning2.png)

Final code - formants.m

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