CLASSIFICATION OF VOCAL TYPES IN CHOIR USING CONVOLUTIONAL RECURRENT NEURAL NETWORK (CRNN)
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 techni...
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id-itb.:741512023-06-26T14:03:49ZCLASSIFICATION OF VOCAL TYPES IN CHOIR USING CONVOLUTIONAL RECURRENT NEURAL NETWORK (CRNN) Stefanus Indonesia Final Project choir, MFCC, stability, timbre, vocal type. INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/74151 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. text |
| institution |
Institut Teknologi Bandung |
| building |
Institut Teknologi Bandung Library |
| continent |
Asia |
| country |
Indonesia Indonesia |
| content_provider |
Institut Teknologi Bandung |
| collection |
Digital ITB |
| language |
Indonesia |
| description |
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. |
| format |
Final Project |
| author |
Stefanus |
| spellingShingle |
Stefanus CLASSIFICATION OF VOCAL TYPES IN CHOIR USING CONVOLUTIONAL RECURRENT NEURAL NETWORK (CRNN) |
| author_facet |
Stefanus |
| author_sort |
Stefanus |
| title |
CLASSIFICATION OF VOCAL TYPES IN CHOIR USING CONVOLUTIONAL RECURRENT NEURAL NETWORK (CRNN) |
| title_short |
CLASSIFICATION OF VOCAL TYPES IN CHOIR USING CONVOLUTIONAL RECURRENT NEURAL NETWORK (CRNN) |
| title_full |
CLASSIFICATION OF VOCAL TYPES IN CHOIR USING CONVOLUTIONAL RECURRENT NEURAL NETWORK (CRNN) |
| title_fullStr |
CLASSIFICATION OF VOCAL TYPES IN CHOIR USING CONVOLUTIONAL RECURRENT NEURAL NETWORK (CRNN) |
| title_full_unstemmed |
CLASSIFICATION OF VOCAL TYPES IN CHOIR USING CONVOLUTIONAL RECURRENT NEURAL NETWORK (CRNN) |
| title_sort |
classification of vocal types in choir using convolutional recurrent neural network (crnn) |
| url |
https://digilib.itb.ac.id/gdl/view/74151 |
| _version_ |
1823652185009815552 |