Training Performance of Recurrent Neural Network using RTRL and BPTT for Gamelan Onset Detection

Abstract

Gamelan is one of Indonesia’s traditional musical instruments. Signal variations in gamelan music are caused by differences in play style and the process of making gamelan. Gamelan music analysis usually using supervised learning method like Recurrent Neural Network (RNN). This paper will compare the performance of Simple Recurrent Neural Network training process using a gradient-based algorithm Backpropagation Through Time (BPTT) and Real-Time Recurrent Learning (RTRL) algorithm. The performance of the algorithm during training process was necessary to be evaluated, in order to know which algorithm has better performance and faster process to approach convergences on the training method of the recurrent neural network. The performance results of the algorithm training process will be compared and evaluated by the means of a Normalized Negative Log-likelihood (NNL). BPTT resulted better and faster forming convergence in terms of the number of epoch parameter with NNL 0.0121 In terms of the value of learning rate, BPTT perform better at learning rate 0.1 with NNL 0.0174 and RTRL performs better at learning rate 0.4 with NNL 0.0382.