End-to-End Automatic Singing Skill Evaluation Using Cross-Attention and Data Augmentation for Solo Singing and Singing With Accompaniment

Yaolong Ju, Chun Yat Wu, Betty Cortiñas-Lorenzo, Jing Yang, Jiajun Deng, Fan Fan, Simon Lui

Abstract: Automatic singing skill evaluation (ASSE) systems are predominantly designed for solo singing, and the scenario of singing with accompaniment is largely unaddressed. In this paper, we propose an end-to-end ASSE system that effectively processes both solo singing and singing with accompaniment using data augmentation, where a comparative study is conducted on four different data augmentation approaches. Additionally, we incorporate bi-directional cross-attention (BiCA) for feature fusion which, compared to simple concatenation, can better exploit the inter-relationships between different features. Results on the 10KSinging dataset show that data augmentation and BiCA boost performance individually. When combined, they contribute to further improvements, with a Pearson correlation coefficient of 0.769 for solo singing and 0.709 for singing with accompaniment. This represents relative improvements of 36.8% and 26.2% compared to the baseline model score of 0.562, respectively.

External IDs:dblp:conf/ismir/JuWCYDFL24