Singing Evaluation based on Deep Metric Learning
Terry Tan · 2019
This paper aims to evaluate singing performance based on deep metric learning. As the vocal sound will be the input, we will first need to separate that from a soundtrack. After the separation, the vocal audio will be represented by Mel-spectrogram as an input in our proposed model. The process to build up our model splits into pre-training and training steps. Meta learning is adopted for pre-training while deep metric learning is adopted for training. The output of the model is a Euclidean distance reflecting the singers' performance, which is determined by comparing their sounds to the originals. Experimental results show a stable and reliable singing evaluation.