Persian Musical Instruments Classification Using Polyphonic Data Augmentation
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866918191908782080 |
|---|---|
| author | Esfangereh, Diba Hadi Sameti, Mohammad Hossein Moridani, Sepehr Harfi Javidpour, Leili Baghshah, Mahdieh Soleymani |
| author_facet | Esfangereh, Diba Hadi Sameti, Mohammad Hossein Moridani, Sepehr Harfi Javidpour, Leili Baghshah, Mahdieh Soleymani |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_05717 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Persian Musical Instruments Classification Using Polyphonic Data Augmentation Esfangereh, Diba Hadi Sameti, Mohammad Hossein Moridani, Sepehr Harfi Javidpour, Leili Baghshah, Mahdieh Soleymani Sound Computation and Language 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. |
| title | Persian Musical Instruments Classification Using Polyphonic Data Augmentation |
| topic | Sound Computation and Language |
| url | https://arxiv.org/abs/2511.05717 |