The range of voices is an essential aspect that a
singer needs to know. This knowledge is necessary so that the
singer can maximize their singing potential. This study
discussed about how to classify someone's vocal range into four
classes commonly used in choir using Mel-frequency Cepstral
Coefficient (MFCC) for its feature extraction and Convolutional
Neural Network (CNN) for the classification. This study
emphasized how MFCC and CNN was able to solve human vocal
type classification problem. It is assisted by WavAugment for
augmentation to maximize the learning process. In this study,
the data used were primary so that the data were collected
through surveys and experiments conducted directly by the
researchers. The data used also affect the classification result,
where the data need to be sparse enough to avoid the model
being overfitted. The experiment is giving a good result where
the training accuracy reaches 91.83% and testing accuracy is
91.14%. This model (specifically the feature extractor) was able
to outperform the STFT model that usually has a competitive
result with 3.11% in training accuracy and 1.15% in testing
accuracy. This study is a multi-disciplinary science that has a
strong influence on music, especially in the choir. This study was
conducted to improve choir music and computer technology
continuity by combining music with computer science.
Keywords— MFCC, CNN, WavAugment, Vocal