Abstract: In modern digital communication, analyzing signals and reducing noise is essential in producing precise and audible sound files, accurately rendering images, or any process in which continuous analog input data needs to be digitized and mathematically manipulated. A prime example is a speech, in which even slight amounts of noise can lead from anywhere to slightly distorted to completely unintelligible sounds. Because human ears are relatively more sensitive to higher-frequency sounds than lower-frequency sounds, we are vulnerable to these unwanted noises, mainly consisting of high-frequency sounds compared to main sound sources such as voice. Thus, to emphasize and enhance the original signal, numerous systems have been designed to diminish noise and amplify the intended signal using filters (which serve to suppress certain characteristics of a signal. These noise-removing systems can be further improved by implementing proper algorithms and mathematical windowing functions to increase effectiveness. In this paper, we employ combinations of a Low Pass Filter (LPF) and various windows to find the best-fit LPF window combination to achieve the highest noise reduction efficiency. We implicated various filter designs at simple trigonometric functions and varying samples to substantiate and illustrate the noise-removing efficiency of nine unique selected filter designs. To collect data, we used MATLAB to analyze the audio files and execute the Fast Fourier Transform onto the original continuous analog voice.
Keywords: Noise reduction, Acoustics, Algorithm, MatLab, Fast Fourier Transform), Low Pass Filter
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