Towards Deep Metric Learning for Singing Assessment¶
Presented at ISMIR 2021
The excellence of human singing is an important aspect of subjective, aesthetic perception of music. In this paper, we propose a novel approach to tackle Automatic Singing Assessment (ASA) task through deep metric learning. With the goal of retrieving the commonalities of good singing without explicitly engineering them, we force a triplet model to map perceptually pleasant-sounding singing performance closer to the reference track compared to others, and thus learning a joint embedding space with performance characteristics. Incorporating mid-level representations like spectrogram and chroma, this approach takes advantage of the feature learning ability of neural networks, while using the reference track as an important anchor. On our designed testing set that spans across various styles and techniques, our model outperforms traditional rule-based ASA systems.