Application of FFT and KNN Methods for the Process of Identifying Sound Signals

Everyone has a different kind of voice. Sound is a unique thing and has a certain range of frequencies and intensity of sound that can and cannot be heard by humans. we can detect important characteristics of the sound. The Fast Fourier Transform (FFT) algorithm is an algorithm for calculating Discrete Fourier Transform (DFT). Process of Identifying Sound Signals beginning with the sound data was preprocessed, feature extraction using FFT, classification using KNN and finding the nearest distance using the Euclidean distance method, an accuracy result of 79% of the tested data was obtained.

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Publication Details

Journal
IC-ITECHS
Published
2024-12-03
DOI
https://doi.org/10.32664/ic-itechs.v5i1.1599
Primary Topic
Flow Measurement and Analysis
Type
article
Field-Weighted Citation Impact
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article

Application of FFT and KNN Methods for the Process of Identifying Sound Signals

Saifulloh Saifulloh, Yessi Yunitasari, Daniswara Andhika Putra Harly
IC-ITECHS
Flow Measurement and Analysis
article

Application of FFT and KNN Methods for the Process of Identifying Sound Signals

Saifulloh Saifulloh, Yessi Yunitasari, Daniswara Andhika Putra Harly
article en

Abstract

Everyone has a different kind of voice. Sound is a unique thing and has a certain range of frequencies and intensity of sound that can and cannot be heard by humans. we can detect important characteristics of the sound. The Fast Fourier Transform (FFT) algorithm is an algorithm for calculating Discrete Fourier Transform (DFT). Process of Identifying Sound Signals beginning with the sound data was preprocessed, feature extraction using FFT, classification using KNN and finding the nearest distance using the Euclidean distance method, an accuracy result of 79% of the tested data was obtained.

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Flow Measurement and Analysis
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