![[2602.20744] Voices of the Mountains: Deep Learning-Based Vocal Error Detection System for Kurdish Maqams](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.20744] Voices of the Mountains: Deep Learning-Based Vocal Error Detection System for Kurdish Maqams
Summary
This article presents a deep learning-based system for detecting vocal errors in Kurdish maqams, addressing the limitations of existing automatic singing assessment tools that primarily follow Western music standards.
Why It Matters
The research highlights the need for culturally relevant music assessment tools, particularly for non-Western musical traditions like Kurdish maqams. By improving error detection in microtonal singing, this study contributes to the preservation and enhancement of Kurdish musical heritage.
Key Takeaways
- Developed a deep learning model to detect vocal errors in Kurdish maqams.
- Identified limitations of existing automatic singing assessment tools for non-Western music.
- Achieved varying levels of accuracy in detecting pitch, rhythm, and modal stability errors.
- Emphasized the need for more data to improve model performance, especially for modal drift errors.
- Contributes to the cultural preservation of Kurdish music through technology.
Computer Science > Sound arXiv:2602.20744 (cs) [Submitted on 24 Feb 2026] Title:Voices of the Mountains: Deep Learning-Based Vocal Error Detection System for Kurdish Maqams Authors:Darvan Shvan Khairaldeen, Hossein Hassani View a PDF of the paper titled Voices of the Mountains: Deep Learning-Based Vocal Error Detection System for Kurdish Maqams, by Darvan Shvan Khairaldeen and Hossein Hassani View PDF HTML (experimental) Abstract:Maqam, a singing type, is a significant component of Kurdish music. A maqam singer receives training in a traditional face-to-face or through self-training. Automatic Singing Assessment (ASA) uses machine learning (ML) to provide the accuracy of singing styles and can help learners to improve their performance through error detection. Currently, the available ASA tools follow Western music rules. The musical composition requires all notes to stay within their expected pitch range from start to finish. The system fails to detect micro-intervals and pitch bends, so it identifies Kurdish maqam singing as incorrect even though the singer performs according to traditional rules. Kurdish maqam requires recognizing performance errors within microtonal spaces, which is beyond Western equal temperament. This research is the first attempt to address the mentioned gap. While many error types happen during singing, our focus is on pitch, rhythm, and modal stability errors in the context of Bayati-Kurd. We collected 50 songs from 13 vocalists ( 2-3 hours) and ...
