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All Journal CESS (Journal of Computer Engineering, System and Science) Journal of Information Technology and Computer Science Jurnal Penelitian Pendidikan IPA (JPPIPA) Martabe : Jurnal Pengabdian Kepada Masyarakat Jurnal RESISTOR (Rekayasa Sistem Komputer) JISKa (Jurnal Informatika Sunan Kalijaga) JURNAL ILMU KOMPUTER INDONESIA Zonasi: Jurnal Sistem Informasi Jurnal Abdi Insani Jurnal Aplikasi dan Inovasi Iptek (JASINTEK) Indonesian Journal of Global Health research GENTA HREDAYA: Media Informasi Ilmiah Jurusan Brahma Widya STAHN Mpu Kuturan Singaraja JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Sugianta, Kadek Arya
Universitas Bali Internasional
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Journal : journal of information technology and computer science

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Comparing Audio and Visual Transfer Learning for Environmental Sound Classification Sugianta, I Kadek Arya
Journal of Information Technology and Computer Science Vol. 11 No. 1: April 2026
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.111841

Abstract

Environmental Sound Classification (ESC) faces significant challenges related to data scarcity and unstructured acoustic signal variability. This study evaluates the effectiveness of a Visual Transfer Learning approach by transforming audio signals into Mel-Spectrogram representations for classification using Computer Vision architectures. A comparative study was conducted on the ESC-50 dataset, benchmarking visual-based models (EfficientNet-B0, ResNet-50) against specialized audio models (Pre-trained Audio Neural Networks/PANNs). Experimental results demonstrate that EfficientNet-B0, optimized with MixUp augmentation, achieved the highest performance with 83.33% accuracy and 83.50% F1-Score, outperforming ResNet-50 (80.00%) and significantly surpassing the PANNs (Cnn14) model, which only reached 66.33%. The underperformance of PANNs indicates issues with over-parameterization on small-scale datasets. Further validation using Gradient-weighted Class Activation Mapping (Grad-CAM) confirmed that the EfficientNet-B0 model precisely learned semantic features by distinguishing active sound patterns from silence and background noise. These findings confirm that lightweight visual architectures possess superior transferability and generalization compared to massive audio models in data-constrained scenarios.
Co-Authors A A Ngr Bagus Aristayudha Dharmayanti, Cokorda Istri I Gede Karang Komala Putra I Gede Wahyu Surya Dharma I Kadek Agus Riki Gunawan I Nyoman Ari Yuda I Wayan Tanjung Aryasa Khadijah Febriana Rukhmanti Udhayana Ni Kadek Winda Patriani Ni Kadek Winda Patrianingsih Ni Luh Putu Surya Astitiani Ni Putu Rahayu Artini Rajdev Dillon Reganata, Gde Palguna Richadinata, Kadek Riyan Putra Vitalia Fina Carla Rettobjaan Widnyani, Ni Made Winda Patrianingsih, Ni Kadek