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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主要作者: Stefanus
格式: Final Project
語言:Indonesia
在線閱讀:https://digilib.itb.ac.id/gdl/view/74151
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id id-itb.:74151
spelling 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