Singing-Voice Timbre Evaluations Based on Transfer Learning

Abstract

The development of artificial intelligence technology has made it possible to realize automatic evaluation systems for singing, and relevant research has been able to achieve accurate evaluations with respect to pitch and rhythm, but research on singing-voice timbre evaluation has remained at the level of theoretical analysis. Timbre is closely related to expression performance, breath control, emotional rendering, and other aspects of singing skills, and it has a crucial impact on the evaluation of song interpretation. The purpose of this research is to investigate the automatic evaluation method of singing-voice timbre. At the present stage, timbre research generally has problems such as a paucity of datasets, a single evaluation index, easy overfitting or a model’s failure to converge. Compared with the singing voice, the research on musical instruments is more mature, with more available data and richer evaluation dimensions. We constructed a deep network based on the CRNN model to perform timbre evaluation, and the test results showed that cross-media learning of timbre evaluation is feasible, which also indicates that humans have a consistent timbre perception with respect to musical instruments and vocals.