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Andri Pranolo
Universitas Ahmad Dahlan
Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Journal : emerging information science and technology

 1 
Audio Noise Reduction Using a U-Net Convolutional Neural Network Architecture Ikhwal, Ikhwal; Pranolo, Andri; Firdausy, Kartika
Emerging Information Science and Technology Vol. 7 No. 1 (2026): May
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/eist.v7i1.33075

Abstract

Audio quality can be degraded by noise contamination, while conventional noise reduction methods such as the Wiener Filter may have limitations in preserving audio spectral characteristics. This study develops an audio noise reduction system using a U-Net Convolutional Neural Network (CNN) based on Short-Time Fourier Transform (STFT) spectrograms to reduce white noise. The dataset consisted of seven accompaniment audio files from the Indonesian Robot Dance Art Contest (KRSTI), with five used for training and two for testing. Clean audio was contaminated with white noise at SNR levels of 0, 5, 10, and 15 dB and transformed into STFT spectrograms, while phase information was retained for reconstruction. The U-Net was trained to learn the mapping between noisy and clean spectrograms and compared with the Wiener Filter as a conventional baseline using SNR, MSE, MAE, LSD, and SI-SDR. The results show that U-Net achieves lower average MSE (0.002453), MAE (0.032514), and LSD (9.663 dB), while the Wiener Filter achieves higher average ΔSNR (3.327 dB) and SI-SDR (12.251 dB) than U-Net, which achieves 1.915 dB and 10.073 dB, respectively. These findings indicate that the two methods exhibit different strengths depending on the evaluation metric. Further research should investigate larger and more diverse datasets, particularly real-world noise conditions, to improve model robustness and generalization.
Co-Authors ., Suparman AA Sudharmawan, AA Abdalla, Modawy Adam Ali Achmad Fanany Onnilita Gaffar Adhi Prahara Adhi Prahara Adhi Prahara, Adhi Adhi Susanto Adhi Susanto Afief Akmal Afiqa, Nurul Agung Bella Putra Utama Agung Bella Utama Putra Agus Cahyono Agus Dianto Agus Salim Ahmad Azhari Aji Prasetya Wibawa Akbari, Ade Kurnia Ganesh Albas, Juan Alin Khaliduzzaman Amelia Ritahani Ismail Anasyua, Fairuz Khairunnisa Andiko Putro Suryotomo Anton Satria Prabuwono Anton Yudhana Ardi Pujiyanta Azhari, Ahmad Azlan, Faris Farhan Ba, Abdoul Fatakhou Bambang Widi Pratolo Camargo, Jair Candra Juni Cahyo Kusuma Dani Fadillah Daniel Happy Putra Drezewski, Rafał Dwiyanto, Felix Andika Elhindi, Mohamed Fachrul Kurniawan Fadhilla, Akhmad Fanny Felix Andika Dwiyanto Firdaus, Nalendra Firdaus, Nalendra Putra Ghazali, Ahmad Badaruddin Hanafi Hanafi Hariyanti, Nunik Heni Pujiastuti Heri Pramono Hoz, César De La Ikhwal, Ikhwal Ismail, Amelia Ritahani Kartika Firdausy Khadir, Mohammed Tarek Leonel Hernandez Leonel Hernandez, Leonel Mao, Yingchi Mirghani, Abdelhameed Mokhtar, Nur Azizah Mohammad Muhammad, Abdullahi Uwaisu Nanang Fitriana Kurniawan Nathalie Japkowicz Nisa, Syed Qamrun Noormaizan, Khairul Akmal Nor Amalina Abdul Rahim Nuril Anwar Nuril Anwar Nuril Anwar, Nuril Nuryana, Zalik Omar, Abdalwahab Omer, Abduelrahman Adam Onie Yudho Sundoro Paramarta, Andien Khansa’a Iffat Prayitno Prayitno Putra, Agung Bella Utama Rafal Drezewski Rafał Dreżewski Rafał Dreżewski Roman Voliansky Saifullah, Shoffan Sarina Sulaiman Sarina Sulaiman Seno Aji Putra Setyaputri, Faradini Usha Snani, Aissa Sri Winiarti Suharto Suharto Sularso Sularso, Sularso Sularso, Sularso Suparman Supriadi Supriadi Supriyanto Taqwa Hariguna Tedy Setyadi Tri Andi Tri Andi Triono, Alfiansyah Putra Pertama Uriu, Wako Utama, Agung Bella Putra Utami, Nurul Putrie Wadiyo Wadiyo Wilis Kaswijanti Yingchi Mao Yingchi Mao Yingchi Mao Yingchi Mao Zalik Nuryana Zhou, Xiaofeng