Human Vocal Type Classification using MFCC and Convolutional Neural Network

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.