Persian Musical Instruments Classification Using Polyphonic Data Augmentation
Diba Hadi Esfangereh, Mohammad Hossein Sameti, Sepehr Harfi Moridani, Leili Javidpour, Mahdieh Soleymani Baghshah

TL;DR
This paper introduces a new dataset and a culturally informed data augmentation method for classifying Persian musical instruments, improving recognition accuracy in polyphonic Persian music.
Contribution
It presents a novel dataset of Persian instruments and a culturally grounded augmentation strategy for polyphonic data, enhancing instrument classification in Persian music.
Findings
Achieved ROC-AUC of 0.795 on real-world polyphonic Persian music
Culturally informed augmentation improves robustness of instrument recognition
Demonstrated effectiveness of combining tonal and temporal features
Abstract
Musical instrument classification is essential for music information retrieval (MIR) and generative music systems. However, research on non-Western traditions, particularly Persian music, remains limited. We address this gap by introducing a new dataset of isolated recordings covering seven traditional Persian instruments, two common but originally non-Persian instruments (i.e., violin, piano), and vocals. We propose a culturally informed data augmentation strategy that generates realistic polyphonic mixtures from monophonic samples. Using the MERT model (Music undERstanding with large-scale self-supervised Training) with a classification head, we evaluate our approach with out-of-distribution data which was obtained by manually labeling segments of traditional songs. On real-world polyphonic Persian music, the proposed method yielded the best ROC-AUC (0.795), highlighting complementary…
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Taxonomy
TopicsMusic and Audio Processing · Music Technology and Sound Studies · Neuroscience and Music Perception
