Sounds useful to make a communication among human beings. Where each sound have characteristics and levels of different frequency. Sounds produced by vocal cords with cooperation of all sound-producing organs. Currently, the application of this technology develops quickly and was very useful for human life, for example: helping hearing impaired, making google translate and the others. There are several problems in sounds identifying sound, like velocity and accuracy of processing voice data.

While for transform sound signals as input signals into sound frequencies in WAV format required a method of Artificial Neural Network to minimize the problem, using Fast Fourier Transform (FFT) with the help of software Matlab to recognize the power of sound (decibels) and a few other voice characteristics. Design goal is to be able to identifying and analyzing characteristics of sound and voice processing data already saved on the system that have been made so that as you wish.

By taking 8 times data on to ten peoples who have recognized sound frequency, the percentage of sound at night more than during day in a row was 75%, 50%, 25%, 37%, and 0% as many as three people, two people, two people, one people, and two people. Differences to this percentage are psychological condition, sound-producing organs and weather conditions on the set of data.