@inproceedings{chen-etal-2026-cantus,
title = "Cantus: Toward Pedagogical Feedback Generation for Sight-Singing Learning",
author = "Chen, Jiale and
Dong, Xuelian and
Chen, Yubing and
Hu, Yongxin and
Xie, Wenxiu and
Hao, Tianyong",
editor = "Zhang, Yue and
Xu, Ruifeng and
Purwarianti, Ayu and
Rong, Tong and
Suzuki, Jun",
booktitle = "Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the {A}sia-Pacific Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = nov,
year = "2026",
address = "Hengqin, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.aacl-long.83/",
pages = "1825--1837",
ISBN = "979-8-89176-439-2",
abstract = "Sight-singing is a fundamental yet challenging task in music education, where timely and fine-grained feedback is crucial for helping learners identify mistakes and improve their performance efficiently. However, existing automatic methods mainly focus on isolated subtasks such as alignment or scoring, and are unable to provide end-to-end diagnostic feedback with explicit identification of pitch, rhythm, and musical fluency errors. In this paper, we propose Cantus, a novel multimodal large language model framework for sight-singing assessment and exercise recommendation generation. Cantus jointly models vocal audio and symbolic musical representations, and leverages a pedagogically grounded error representation module together with a large language model to diagnose performance errors, generate structured assessment comments, and provide personalized exercise recommendations. To support this task, we construct a new dataset MIVO (MIDI-Voice) containing 1,003 real-world sight-singing samples with aligned MIDI notes and vocal recordings collected from authentic teaching scenarios. Experimental results demonstrate the effectiveness of Cantus in sight-singing assessment, achieving strong performance across multiple metrics. Human evaluation of feedback quality shows the potential of Cantus in supporting student-centered sight-singing learning."
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<abstract>Sight-singing is a fundamental yet challenging task in music education, where timely and fine-grained feedback is crucial for helping learners identify mistakes and improve their performance efficiently. However, existing automatic methods mainly focus on isolated subtasks such as alignment or scoring, and are unable to provide end-to-end diagnostic feedback with explicit identification of pitch, rhythm, and musical fluency errors. In this paper, we propose Cantus, a novel multimodal large language model framework for sight-singing assessment and exercise recommendation generation. Cantus jointly models vocal audio and symbolic musical representations, and leverages a pedagogically grounded error representation module together with a large language model to diagnose performance errors, generate structured assessment comments, and provide personalized exercise recommendations. To support this task, we construct a new dataset MIVO (MIDI-Voice) containing 1,003 real-world sight-singing samples with aligned MIDI notes and vocal recordings collected from authentic teaching scenarios. Experimental results demonstrate the effectiveness of Cantus in sight-singing assessment, achieving strong performance across multiple metrics. Human evaluation of feedback quality shows the potential of Cantus in supporting student-centered sight-singing learning.</abstract>
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%0 Conference Proceedings
%T Cantus: Toward Pedagogical Feedback Generation for Sight-Singing Learning
%A Chen, Jiale
%A Dong, Xuelian
%A Chen, Yubing
%A Hu, Yongxin
%A Xie, Wenxiu
%A Hao, Tianyong
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-439-2
%F chen-etal-2026-cantus
%X Sight-singing is a fundamental yet challenging task in music education, where timely and fine-grained feedback is crucial for helping learners identify mistakes and improve their performance efficiently. However, existing automatic methods mainly focus on isolated subtasks such as alignment or scoring, and are unable to provide end-to-end diagnostic feedback with explicit identification of pitch, rhythm, and musical fluency errors. In this paper, we propose Cantus, a novel multimodal large language model framework for sight-singing assessment and exercise recommendation generation. Cantus jointly models vocal audio and symbolic musical representations, and leverages a pedagogically grounded error representation module together with a large language model to diagnose performance errors, generate structured assessment comments, and provide personalized exercise recommendations. To support this task, we construct a new dataset MIVO (MIDI-Voice) containing 1,003 real-world sight-singing samples with aligned MIDI notes and vocal recordings collected from authentic teaching scenarios. Experimental results demonstrate the effectiveness of Cantus in sight-singing assessment, achieving strong performance across multiple metrics. Human evaluation of feedback quality shows the potential of Cantus in supporting student-centered sight-singing learning.
%U https://aclanthology.org/2026.aacl-long.83/
%P 1825-1837
Markdown (Informal)
[Cantus: Toward Pedagogical Feedback Generation for Sight-Singing Learning](https://aclanthology.org/2026.aacl-long.83/) (Chen et al., AACL-IJCNLP 2026)
ACL
- Jiale Chen, Xuelian Dong, Yubing Chen, Yongxin Hu, Wenxiu Xie, and Tianyong Hao. 2026. Cantus: Toward Pedagogical Feedback Generation for Sight-Singing Learning. In Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1825–1837, Hengqin, China. Association for Computational Linguistics.