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 annotated 221 error spans (150 fine pitch, 46 rhythm, 25 modal drift). The data was segmented into 15,199 overlapping windows and converted to log-mel spectrograms. We developed a two-headed CNN-BiLSTM with attention mode to decide whether a window contains an error and to classify it based on the chosen errors. Trained for 20 epochs with early stopping at epoch 10, the model reached a validation macro-F1 of 0.468. On the full 50-song evaluation at a 0.750 threshold, recall was 39.4% and precision 25.8% . Within detected windows, type macro-F1 was 0.387, with F1 of 0.492 (fine pitch), 0.536 (rhythm), and 0.133 (modal drift); modal drift recall was 8.0%. The better performance on common error types shows that the method works, while the poor modal-drift recall shows that more data and balancing are needed.
Abstract (translated)
玛卡姆（Maqam）是库尔德音乐中的一个重要组成部分，是一种歌唱类型。玛卡姆歌手通常通过传统的面对面指导或自我学习接受训练。自动歌唱评估（ASA）使用机器学习来提供对演唱风格准确性的评价，并能帮助学习者通过检测错误改进其表现。当前可用的ASA工具遵循西方音乐规则。这些系统要求所有音符在整个持续时间内的音高范围内保持不变，无法识别微间隔和音高的弯曲，因此即使歌手按照传统规则演唱，该系统的判定也可能认为库尔德玛卡姆歌唱是不正确的。库尔德玛卡姆需要在微调音空间中识别表演错误，这超出了西方等温律的范围。 本研究首次尝试解决上述差距。尽管在唱歌过程中会出现多种类型的错误，但我们的重点是在拜亚提-库尔（Bayati-Kurd）语境下对音高、节奏和模态稳定性的错误进行分析。我们从13名歌手那里收集了50首歌曲（每人大约2至3小时），并标注了221个错误片段（其中包括150个细音高错误，46个节奏错误以及25个调式漂移）。数据被分割成重叠的15,199个窗口，并转换为对数梅尔频谱图。我们开发了一种双头卷积神经网络-双向长短期记忆模型（CNN-BiLSTM）带注意力模式，用于判定一个窗口是否包含错误并根据选定的标准对其进行分类。 经过20轮训练，在第10轮时提前停止了训练，模型在验证阶段的宏平均F1得分为0.468。在50首歌曲全部进行评估且阈值设为0.750的情况下，召回率为39.4%，精度为25.8%。对于检测到的窗口，类型宏平均F1得分为0.387，具体而言，音高（Fine pitch）类型的F1得分为0.492，节奏（Rhythm）类型的F1得分是0.536，调式漂移（Modal drift）类型的F1得分仅为0.133；调式漂移的召回率更是低至8.0%。虽然对于常见的错误类型显示了较好的性能表明该方法有效，但较低的模态漂移召回率则显示出需要更多数据和平衡以改进评估结果。 这段研究总结描述了一种新开发的方法，旨在改善自动歌唱评估系统在识别库尔德玛卡姆音乐中特定误差方面的效果，并指出了进一步优化的方向。
URL
https://arxiv.org/abs/2602.20744
