Automatic Assessment Of Singing Voice Pronunciation: A Case Study With Jingju Music

Rong Gong, Xavier Serra
2018 Zenodo  
Online learning has altered music education remarkable in the last decade. Large and increasing amount of music performing learners participate in online music learning courses due to the easy-accessibility and boundless of time-space constraints. However, online music learning cannot be extended to a large-scale unless there is an automatic system to provide assessment feedback for the student music performances. Singing can be considered the most basic form of music performing. The critical
more » ... le of singing played in music education cannot be overemphasized. Automatic singing voice assessment, as an important task in Music Information Research (MIR), aims to extract musically meaningful information and measure the quality of learners' singing voice. Singing correctness and quality is culture-specific and its assessment requires culture-aware methodologies. Jingju (also known as Beijing opera) music is one of the representative music traditions in China and has spread to many places in the world where there are Chinese communities. The Chinese tonal languages and the strict conventions in oral transmission adopted by jingju singing training pose unique challenges that have not been addressed by the current MIR research, which motivates us to select it as the major music tradition for this dissertation. Our goal is to tackle unexplored automatic singing voice assessment problems in jingju music, to make the current eurogeneric assessment approaches more culture- aware, and in return, to develop new assessment approaches which can be generalized to other music traditions. This dissertation aims to develop data-driven audio signal processing and machine learning (deep learning) models for automatic singing voice assessment in audio collections of jingju music. We identify challenges and opportunities, and present several research tasks relevant to automatic singing voice assessment of jingju music. Data-driven computational approaches require well-organized data for model training and testing, and we report the p [...]
doi:10.5281/zenodo.1490343 fatcat:f3mrhstkdff6ppmdadeasfuo7m