End-to-End Automatic Singing Skill Evaluation Using Cross-Attention and Data Augmentation for Solo Singing and Singing With Accompaniment
Year: 2024
Type: conference-paper
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.
Authors: Yaolong Ju, Chun Yat Wu, Betty Cortiñas Lorenzo, Jing Yang, Jiajun Deng, Fan Fan, Simon Lui
Language: English
Topic: Music and Audio Processing
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6911778445
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