Persian Musical Instruments Classification Using Polyphonic Data Augmentation

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 benefits of tonal and temporal coherence. These results demonstrate the effectiveness of culturally grounded augmentation for robust Persian instrument recognition and provide a foundation for culturally inclusive MIR and diverse music generation systems.