CLASSIFICATION OF VOCAL TYPES IN CHOIR USING CONVOLUTIONAL RECURRENT NEURAL NETWORK (CRNN) - Stefanus
Determining vocal types is a crucial aspect that forms the foundation of choir composition. This determination requires an in-depth understanding of music. This reality makes the construction of a vocal type classification model in a choir a multidisciplinary experiment. In addition to the technical review, a profound musicality review is also needed. Consequently, determining vocal types in a choir cannot be done merely based on rules regarding the range of an individual's voice. This research focuses on classifying vocal types using features other than the fundamental frequency (f0), such as timbre and vocal stability. In this study, data were collected from the members of the ITB Student Choir (PSM-ITB), with the labeling process carried out by the ITB Student Choir (PSM-ITB) training team. This study provides relatively good results compared to the previous models. The Convolutional Recurrent Neural Network model achieved an accuracy of 0.87, higher than the Convolutional Neural Network model with an accuracy of 0.74, and the Recurrent Neural Network model with an accuracy of 0.65.