Olabisi Alao

Olabisi Alao has an uncanny love for numbers, which he discovered at an early age, trying different operations anywhere he found them, including license plates, toys, billboards, and books. His explorations led to interests in problem solving and technology management. Olabisi has done groundbreaking work in telemetry, cybercrime, manufacturing process management, banking operations, and election monitoring using GIS.

He is currently a computational maths and economics student at the Rochester Institute of Technology in New York. Olabisi loves operations research and information system management. He loves research and coming up with revolutionary solutions. He enjoys movies, billiards, reading, thinking, and exploring new ways of doing things.

Sound Print and Sensation

My project examined elements and characteristics of sound in order to discover recognizable patterns. The goal was to classify sound sources and come up with an ideal structure or fingerprint in NKS, and to come up with a distinguishable representation for these sounds.

This exercise scrutinized the properties of the different sound waves for: the structure of the structure of sound sources, frequency, amplitude, and time; the representation of sound using wave function and waveform; the different formats for the sound files such as .wav, .mp3, .aiff, and .au; and the intrinsic data contained in each waveform.

The main tools used to mine the sound sources are Fourier analysis and frequency binning. We analyzed the sound by manipulating the Fourier spectrum, the sonogram, and the raw waveform. Fourier analysis is a powerful tool for sound sequencing and pattern recognition. It has been used to break down the multiple elements of the sound and to solve problems of symbolic sequence, classification, and clustering.

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