FOURIER TRANSFORM AND ITS APPLICATION TO SIGNAL PROCESSING
Abstract
ABSTRACT
This paper presents a numerical approach to filtering an audio signal to remove unwanted high- frequency components above 2,000 Hz, thereby improving signal quality. The process begins with spectral analysis, where the Discrete Fourier Transform (DFT) is applied to extract the magnitude and phase spectra. Key spectral features, including spectral centroid, bandwidth, and roll-off, are computed to characterize the frequency distribution of the signal. A low-pass finite impulse response (FIR) filter is then designed and applied using convolution to attenuate high- frequency noise. The results show that the filtering process effectively smooths the signal while preserving its essential low-frequency components. This study demonstrates the importance of spectral analysis and filtering techniques in enhancing audio signals for improved clarity and usability in signal processing applications.
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