Week 6 Voice Recognition Update

Development

Development of the Voice Recognition module is nearly halfway complete.  Initially, an overview of the process for the final algorithm was planned and is as follows:

1. The user creates the training database by recording 10 samples of he/she saying an arbirtray string sequence. 
2. Matlab converts, analyzes (converts from time to frequencey domain via Fast Fourier Transform (FFT)), and saves these as .wav files.
3. When the user tries to login he is prompted to repeat the certain sequence.
4. Matlab conducts FFT on the sample and then compares it to saved file top determine users identity.

http://www.osu-tulsa.okstate.edu/istr/blockdiagramsgrad.jpg

At this point, progress is on fine tuning step 3 and 1 and 2 have been completed. So far, the project consists of two Matlab (.m) files that have to be executed separately.   One for establishing identity and the other for testing it. 

To test and verify outcome, a standard PC microphone manufactured by General Electric is being used.



http://ecx.images-amazon.com/images/I/21H7wzsa2eL._SL500_SS100_.jpg

A Look at Fourier Analysis

At the core of the algorithm is the Fourier Analysis.   A common tool used today, it was developed by the mathematician Joseph Fourier.  Fourier proved that any continuous function could be produced as an infinite sum of sine and cosine waves.  Using this information we can breakdown sound into its components and systematically analyze it. 

To accomplish this Matlab will be programmed to convert the sound wave from the time domain to the frequency domain as shown below:


http://hyperphysics.phy-astr.gsu.edu/hbase/audio/Fourier.html#c1

This picture shows the amplitudes of the individual components of the sound file displayed against their frequency.  It illustrates the power of a Fourier Transform in dealing with sound waves.

In summary, progress of the voice recognition module is proceeding as planned in the proposal.