Regularized DTW in Offline Music Score-Following for Sight-Singing Based on Sol-fa Name Recognition

Abstract

The automatic scoring of singing evaluation is a hot topic in recent years. Improving the score following effect is the first step to improve the accuracy of evaluation. Most of the commonly used methods are based on DTW, but for audios with low singing quality and inaccurate pitch, DTW often predicts the onset incorrectly. In order to solve the above problems, this paper focus on the offline following, mainly improves from two aspects: 1. Sol-fa name recognition is done before pitch tracking as preprocess. We cannot guarantee that the pitch of the singer is correct, but we can assume that the singer pronounces the sol-fa name correctly, so we use sol-fa name recognition as preprocessing; 2. Regularized DTW is proposed based on the basis of sol-fa name recognition. The results show that for general audio, under the condition of a tolerance of 20ms, compared with about 86% accuracy of ordinary DTW algorithm, our algorithm has improved to about 92%, while the average error of predicted notes is reduced by about 23ms. For audio with low signal-to-noise ratio and unstable voice frequency, the alignment effect is improved by about 20% compared with ordinary DTW.