Implementation of Fast Fourier Transform and Least Mean Square Algorithms in The Denoising Process of Audio Signal

Putri Rahmasari Rayes; Nuzla Af'idatur Robbaniyyah; Syamsul Bahri

doi:10.29303/emj.v8i1.255

Authors

Putri Rahmasari Rayes Department of Mathematics, Universitas Mataram
Nuzla Af'idatur Robbaniyyah Department of Mathematics, Universitas Mataram
Syamsul Bahri Department of Mathematics, Universitas Mataram

DOI:

https://doi.org/10.29303/emj.v8i1.255

Keywords:

Audio signal, denoising process, FFT, LMS, signal to noise ratio, mean square error

Abstract

Audio signals play an important role as a medium for storing information, such as lecture materials, interview results, and other archives. However, audio signals are often contaminated by noise, which is unwanted interference that can affect their quality. Therefore, a denoising process is needed to reduce or eliminate noise components in the signal. The Fast Fourier Transform (FFT) and Least Mean Square (LMS) algorithms are frequently used in the denoising process due to their simple and easy-to-implement steps. This research uses primary data, specifically audio signals recorded under two noise conditions: rain noise as Audio Signal 1 and guitar instrument noise as Audio Signal 2, both stored in WAV format. The denoising process was performed using MATLAB software and evaluated based on Signal-to-Noise Ratio (SNR) and Mean Squared Error (MSE) metrics. Higher SNR values and lower MSE values indicate the success of the denoising process in improving audio signal quality. The results of this study demonstrate the effectiveness of the applied algorithms, where the SNR value reached 38.2596 dB with an MSE of 0.0000028211 for Audio Signal 1, and an SNR value of 38.6881 dB with an MSE of 0.0000014988 for Audio Signal 2. An SNR value between 25 dB and 40 dB is categorized as a very good signal, indicating that the quality of the processed audio signals falls into the very good signal category.

References

R. Y. Sipasulta, A. S. M. Lumenta, and S. R. U. A. Sompie, “Simulasi Sistem Pengacak Sinyal Dengan Metode FFT (Fast Fourier Transform),” E-Journal Teknik Elektro Dan Komputer, vol. 3, no. 2, pp. 1–9, 2014. https://doi.org/10.35793/jtek.v3i2.4448.

S. T. Wahyudi, E. Safrianti, and Y. Rahayu, “Aplikasi Spectrum Analyzer untuk Menganalisis Frekuensi Sinyal Audio Menggunakan MATLAB,” Jom FTEKNIK, vol. 2, no. 2, 2015. https://jnse.ejournal.unri.ac.id/index.php/JOMFTEKNIK/article/view/7964.

S. Wirawan and E. Prasetyo, “Implementasi Metode Noise Gate, Low Pass Filter, dan Silent Removal untuk Menghilangkan Noise pada File Suara Menggunakan Parameter Dinamis,” Jurnal Ilmiah Teknologi Rekayasa, vol. 21, no. 3, pp. 152–162, 2017. https://ejournal.gunadarma.ac.id/index.php/tekno/article/view/1594.

S. Gunawan, E. S. Rahman, and Mardhatillah, “Metode Penentuan Perbaikan Noise Pada Data Musik Menggunakan Algoritma Least Mean Square,” Journal of Digital Technology and Computer Science, vol. 01, no. 1, pp. 48–56, 2023. https://doi.org/10.61220/digitech.v1i1.20235.

N. D. Pah, Pemrosesan Sinyal Digital. Surabaya, Indonesia: Graha Ilmu, 2018. .

R. M. Untsa, F. S. Akbar, H. Briantoro, N. Rachmaningrum, and U. Mustakim, “Filter Least Mean Square (LMS) untuk Mengurangi Noise pada Sinyal SuaraTembakan,” Proceedings of the National Conference on Electrical Engineering, Informatics, Industrial Technology, and Creative Media, vol. 3, no. 1, pp. 104–112, 2023. https://conferences.ittelkom-pwt.ac.id/index.php/centive/article/view/165.

A. A. Bimantara, M. S. Adhi, D. Priambodo, H. M. Azhar, and A. Junaidi, “Simulasi Penghilangan Noise Pada Sinyal Suara Menggunakan Metode Fast Fourier Transform (FFT),” Journal of Informatics, Information System, Software Engineering and Applications (INISTA), vol. 1, no. 2, pp. 20–25, 2019. http://doi.org/10.20895/inista.v1i2.45.

Y. Wang and S. Jia, “MADRAS-NET: A deep learning approach for detecting and classifying android malware using Linknet,” Measurement: Sensors, vol. 33, p. 101113, 2024. https://doi.org/10.1016/j.measen.2024.101113.

L. R. Rizkina, Edwar, and L. O. Nur, “Perancangan dan implementasi pengolahan sinyal radar untuk pengukuran doppler, range, dan sar imaging menggunakan raspberry pi.”

M. W. Setiawan, L. Novamizanti, and I. N. A. Ramatryana, “Pemisahan chorus pada musik mp3 menggunakan koefisien korelasi 2-d berbasis discrete cosine transform (dct) dan k-nearest neighbor (k-nn).”

E. O. Brigham, The Fast Fourier Transform and Its Applications. Prentice Hall Signal Processing Series, Englewood Cliffs, N.J: Prentice-Hall, 1988. .

S. S. Randhawa, “Analysing & Implementing Cooley Tukey Fast Fourier Transform Algorithm,” Research Gate, pp. 1–4, 2018. http://doi.org/10.13140/RG.2.2.19037.33767.

M. H. Hayes, Statistical Digital Signal Processing and Modeling. New York: John Wiley & Sons, 1996. .

H. G. Alfarizi, J. Raharjo, and J. H. Manurung, “Noise Cancellation of Speech Signal Using Dual Microphone System with Discrete Cosine Transform Least Mean Square Algorithm,” e-Proceeding of Engineering, vol. 5, no. 2, pp. 2161–2168, 2018. https://openlibrarypublications.telkomuniversity.ac.id/index.php/engineering/article/view/6641/0.

Khairunnisa, Sarifudin, and Z. Ahyadu, “Uji Kualitas Sinyal Audio dengan Metode Fourier dan Metode Wevelet,” Seminar Nasional Terapan Riset Inovatif, vol. 7, no. 1, pp. 555–562, 2021. .

F. N. Rahmawati, A. Hambali, and M. I. Maulana, “Analisis kinerja kanal berkabut pada free space optics.” https://openlibrary.telkomuniversity.ac.id/home/catalog/id/155819/slug/analisis-kinerja-kanal-berkabut-pada-free-space-optics.html.

E. I. Alwi, “Analisis Kualitas Sinyal WiFi pada Universitas Muslim Indonesia,” INFORMAL: Informatics Journal, vol. 4, no. 1, pp. 30–39, 2019. http://doi.org/10.19184/isj.v4i1.10153.

A. D. Hendrata and A. Prihanto, “Analisis Kualitas Suara Stego Audio Penyisipan Informasi Tersembunyi dengan Metode Least Significant Bit,” Journal of Informatics and Computer Science (JINACS), vol. 2, no. 03, pp. 178–184, 2021. https://doi.org/10.26740/jinacs.v2n03.p178-184.

A. Fitriatul Aisyah and A. Noortjahja, “Implementasi Hidden Markov Models (HMM) sebagai Filter untuk Mereduksi Noise pada Esophageal Speech,” Jurnal Inovasi Fisika Indonesia, vol. 4, no. 3, pp. 7–14, 2015. https://doi.org/10.26740/ifi.v4n3.p%p.

D. H. Tanjung, “Jaringan Saraf Tiruan dengan Backpropagation untuk Memprediksi Penyakit Asma,” Creative Information Technology Journal, vol. 2, no. 1, p. 28, 2015. https://doi.org/10.24076/citec.2014v2i1.35.