Composing Music with Grammar Argumented Neural Networks and Note-Level Encoding

TL;DR

This paper introduces a novel music composition method combining LSTM neural networks with music theory grammars, using note-level encoding to generate more natural and theory-compliant music.

Contribution

It proposes a new approach that integrates grammar-augmented filters with LSTM, encoding pitches and durations as a single entity for improved music generation.

Findings

Generated music shows high diatonic scale usage

Music exhibits small pitch intervals and chords

Approach outperforms traditional LSTM in musicality

Abstract

Creating aesthetically pleasing pieces of art, including music, has been a long-term goal for artificial intelligence research. Despite recent successes of long-short term memory (LSTM) recurrent neural networks (RNNs) in sequential learning, LSTM neural networks have not, by themselves, been able to generate natural-sounding music conforming to music theory. To transcend this inadequacy, we put forward a novel method for music composition that combines the LSTM with Grammars motivated by music theory. The main tenets of music theory are encoded as grammar argumented (GA) filters on the training data, such that the machine can be trained to generate music inheriting the naturalness of human-composed pieces from the original dataset while adhering to the rules of music theory. Unlike previous approaches, pitches and durations are encoded as one semantic entity, which we refer to as note-level encoding. This allows easy implementation of music theory grammars, as well as closer emulation of the thinking pattern of a musician. Although the GA rules are applied to the training data and never directly to the LSTM music generation, our machine still composes music that possess high incidences of diatonic scale notes, small pitch intervals and chords, in deference to music theory.