Automatic Evaluation of Singing Quality without a Reference

Abstract

Automatic singing quality evaluation methods currently rely on reference singing vocals or score information for comparison. However singers may deviate from the reference singing vocal to personalize the singing that still sounds good. In this work, we present pitch histogram-based methods to automatically evaluate singing quality without any reference singing or score information. We validate the methods with the help of human ratings, and compare with the baseline methods of singing evaluation without a reference. We obtain an average Spearman's rank correlation of 0.716 with human judgments.