Wei Xu
ABSTRACT
Based on the smart phone, this paper integrates OMR (Optical Music Recognition) with sight-singing evaluation, and develops a "shoot and sing" practice APP called SingMaster. This system is mainly composed of three modules: OMR, evaluation and user interface. The OMR module converts the score photographed in the real scene into a note reference sequence. The sight-sing evaluation module first completes the note transcription of the sound spectrum through onset detection and pitch extraction, then aligns the transcribed note sequence with the reference sequence, and performs the evaluation. Finally, the evaluation results are visually fed back to the practitioners through the user interface module. It can provide guidance for practitioners at any time, any place and on any score instead of a real teacher.
Supplemental Material
Available for Download
- Payne, Dorothy. Essential skills for promoting a lifelong love of music and music making. The American Music Teacher 54, 4 (2005), pp 26.Google Scholar
- Jong Wook Kim, Justin Salamon, Peter Li, and Juan Pablo Bello. Crepe: A convolutional representation for pitch estimation. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2018, pp. 161--165.Google ScholarDigital Library
- Sungkyun Chang, Kyogu Lee. A pairwise approach to simultaneous onset/offset detection for singing voice using correntropy. In 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2014, pp. 629--633.Google ScholarCross Ref
- Chun-Ta Chen, Jyh-Shing Roger Jang, and Wenshan Liou. Improved scoreperformance alignment algorithms on polyphonic music. In 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2014, pp. 1365--1369.Google ScholarCross Ref
- Telmo Pinto, Ana Rebelo, Gilson Giraldi, and Jaime S. Cardoso. Music score binarization based on domain knowledge. In Iberian Conference on Pattern Recognition and Image Analysis. Springer, 2011, pp. 700--708.Google ScholarCross Ref
- Calvo-Zaragoza, Jorge, and David Rizo. End-to-end neural optical music recognition of monophonic scores. Applied Sciences 8, 4(2018), pp. 606.Google Scholar
- Weiming Yang, Xianke Wang, Bowen Tian, Wei Xu and Wenqing Cheng. A Multi-stage Automatic Evaluation System for Sight-singing. IEEE Transactions on Multimedia, 2022, doi: 10.1109/TMM.2022.3168132.Google Scholar
Index Terms
- SingMaster: A Sight-singing Evaluation System of "Shoot and Sing" Based on Smartphone
Recommendations
Creating an A Cappella Singing Audio Dataset for Automatic Jingju Singing Evaluation Research
DLfM '17: Proceedings of the 4th International Workshop on Digital Libraries for MusicologyThe data-driven computational research on automatic jingju (also known as Beijing or Peking opera) singing evaluation lacks a suitable and comprehensive a cappella singing audio dataset. In this work, we present an a cappella singing audio dataset which ...
A Query-by-Singing System for Retrieving Karaoke Music
This paper investigates the problem of retrieving karaoke music using query-by-singing techniques. Unlike regular CD music, where the stereo sound involves two audio channels that usually sound the same, karaoke music encompasses two distinct channels ...
A Multi-Stage Automatic Evaluation System for Sight-Singing
Sight-singing exercises are a fundamental part of music education. In this paper, we present an objective and complete automatic evaluation system for sight-singing, which has two critical stages: note transcription and note alignment. In the first stage, ...
Comments