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Kelvin Wyeth
Bina Nusantara University

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A hybrid CNN-autoencoder-SVM/XGBoost model for polyphonic orchestral instrument classification Kelvin Wyeth; Iman Herwidiana Kartowisastro
Indonesian Journal of Electrical Engineering and Computer Science Vol 43, No 1: July 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v43.i1.pp114-126

Abstract

Polyphonic orchestral recordings pose significant challenges in music information retrieval (MIR) due to their overlapping frequency ranges and timbral similarities among instrument families, which complicate multi-label instrument classification. Prior studies have explored the integration of convolutional neural networks (CNN)-based feature extraction with classical machine learning (ML) classifiers, often on monophonic or simpler datasets like IRMAS. But the integration of deep learning (DL) feature extraction, Autoencoder (AE)-based dimensionality reduction, and ML classifiers for polyphonic orchestral instrument recognition remains underexplored. This study proposes a hybrid framework utilizing a pre-trained Inception V3 CNN for feature extraction from mel-spectrograms, followed by an optional 50% dimensionality reduction via AE, and finally, classification with support vector machines (SVM) or extreme gradient boosting (XGBoost). Experiments were run on two polyphonic datasets, OpenMIC-2018 and Orchset. The results demonstrate that non-AE configurations generally outperform AE variants. These results extend prior studies such using polyphonic datasets. The results highlight the practical value of hybrid CNN-ML pipelines and the trade-offs of feature compression in MIR.
Co-Authors Iman Herwidiana Kartowisastro