# Diagonal RNNs in Symbolic Music Modeling

## TL;DR

This paper introduces a diagonal RNN architecture that replaces full recurrent matrices, leading to improved test likelihood and faster convergence in symbolic music modeling tasks across various RNN types.

## Contribution

The paper presents a simple yet effective modification of RNNs using diagonal matrices, enhancing performance and training speed in symbolic music modeling.

## Key findings

- Diagonal RNNs outperform full RNNs in test likelihood
- Faster convergence observed with diagonal matrices
- Effective across LSTM, GRU, and vanilla RNNs

## Abstract

In this paper, we propose a new Recurrent Neural Network (RNN) architecture. The novelty is simple: We use diagonal recurrent matrices instead of full. This results in better test likelihood and faster convergence compared to regular full RNNs in most of our experiments. We show the benefits of using diagonal recurrent matrices with popularly used LSTM and GRU architectures as well as with the vanilla RNN architecture, on four standard symbolic music datasets.