MELODY CLASSIFICATION OF POPULAR MUSIC USING ARTIFICIAL NEURAL NETWORK WITH BACKPROPAGATION

• Main Author: Riasdita Valentina, Cornelia
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/76503
• Institution: Institut Teknologi Bandung

This research is about classification of melody from popular music, using artificial neural network (ANN) with backpropagation. The melodies are restricted to be only the vocal part of popular music. The parts of melody that are included in the dataset are the ones that related to the pitch and duration. The classification has three classes, good melody, ordinary melody, and bad melody. The sets of melodies and the ratings used for network training are obtained from an online questionnaire. Obtained data then transcribed into a music score format and transposed into the scale of C. All the melodies need to be encoded into a form that can be processed by the network. The encoding format is divided into general information of the melody and note information. General information of the melody consists of parameters such as tempo, time signature, and anacrusis. Every note information contains parameters such as pitch of the note, accidentals of a note, octave position of a note, and duration of a note. In this research, the deep learning toolkit from MATLAB is used. Mean squared error (MSE) is used as the loss function. For algorithm of the backpropagation, gradient descent with momentum and adaptive learning rate (traingdx) is used. The architecture of the network consists of 280 input neurons, a hidden layer that contains 7 neurons, and one output neuron. Sets of melodies that are never been heard by the respondents and the network are used. The rating of testing melodies are obtained through a second questionnaire. After the training, only 48% of the prediction match the rating from the second questionnaire. It is suspected that the encoding note by note format is not suitable for melody classification and the size of the training dataset is not large enough.