INTRODUCTION:

The ability to tell apart different sounds has the potential to be very useful in many fields of science and everyday life. Examples of its applications include speech recognition, surveillance, entertainment and media analysis and artificial intelligence.
Various methods of sound recognition have been devised albeit with limited success but because of its potential worth, a lot has been invested to find better ways of distinguishing different sounds.

APPROACH:

In this project, we have several sound clips with very different properties as our raw data. They are divided into small 5 second samples for analysis. The analysis takes part in three steps.

1. Representing the sound in the frequency domain.
2. Extracting features that could distinguish sound.
3. Clustering the sounds according to these features. 

If the sounds are clustered correctly, we should have sounds that sound similar being clustered together which would imply sound recognition!

Please visit this page for a step-by-step explanation of how the program works.