# Persian Musical Instruments Classification Using Polyphonic Data Augmentation

arXiv: https://arxiv.org/abs/2511.05717 · HTML: https://mlsanity.com/paper/2511.05717/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 27 references that are in the map.
- First posted on arXiv 7 Nov 2025.

## Builds on (most cited first, 8 of 8)

- [Contrastive Learning of Musical Representations](https://mlsanity.com/paper/2103.09410/) (2021)
- [LLark: A Multimodal Instruction-Following Language Model for Music](https://mlsanity.com/paper/2310.07160/) (2023)
- [Augmentation Methods on Monophonic Audio for Instrument Classification in Polyphonic Music](https://mlsanity.com/paper/1911.12505/) (2019)
- [Instrument-Independent Dastgah Recognition of Iranian Classical Music Using AzarNet](https://mlsanity.com/paper/1812.07017/) (2018)
- [Iranian Modal Music (Dastgah) detection using deep neural networks](https://mlsanity.com/paper/2203.15335/) (2022)
- [The Persian Piano Corpus: A Collection Of Instrument-Based Feature Extracted Data Considering Dastgah](https://mlsanity.com/paper/2311.11074/) (2023)
- [Machine Learning Techniques in Automatic Music Transcription: A Systematic Survey](https://mlsanity.com/paper/2406.15249/) (2024)
- [A Hierarchical Deep Learning Approach for Minority Instrument Detection](https://mlsanity.com/paper/2506.21167/) (2025)

## Built on by (most cited first, 2 of 2)

- [PARSA-Bench: A Comprehensive Persian Audio-Language Model Benchmark](https://mlsanity.com/paper/2603.14456/) (2026)
- [Persian MusicGen: A Large-Scale Dataset and Culturally-Aware Generative Model for Persian Music](https://mlsanity.com/paper/2605.14765/) (2026)

## Abstract (arXiv)

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.