Abstract

When developing deep learning models for melody extraction, MedleyDB is a must-have dataset owing to its scale, diverse genres, and quality. However, it has no ground-truth labels for vocal melody. At the same time, none of existing methods for vocal melody labeling is able to accurately label vocal melody. In this paper we propose a simple, correct method for this task. Through extensive experiment, we evaluate the influence of labeling on the performance of models for vocal melody extraction, and find that in most cases the proposed method can boost the performance. The proposed method can accelerate research in relevant areas.