Persian Musical Instruments Classification Using Polyphonic Data Augmentation

• URL: http://arxiv.org/abs/2511.05717v1
• Date: Fri, 07 Nov 2025 21:18:42 GMT
• Title: Persian Musical Instruments Classification Using Polyphonic Data Augmentation
• Abstract summary: We introduce a new dataset of isolated recordings covering seven traditional Persian instruments and two common but originally non-Persian instruments.<n>We propose a culturally informed data augmentation strategy that generates realistic polyphonic mixtures from monophonic samples.
• Score: 8.72752668537241
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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