Melody Generation using an Interactive Evolutionary Algorithm

TL;DR

This paper introduces an interactive evolutionary algorithm for melody generation that combines human scoring with neural network evaluation, producing stylistically pleasing music efficiently.

Contribution

It presents a novel hybrid approach integrating human expertise, Bi-LSTM neural networks, and genetic algorithms for music creation and evaluation.

Findings

Successfully generated melodies with desired styles.

Achieved faster performance than existing data-driven evolutionary systems.

Demonstrated the effectiveness of combining human scoring with neural network evaluation.

Abstract

Music generation with the aid of computers has been recently grabbed the attention of many scientists in the area of artificial intelligence. Deep learning techniques have evolved sequence production methods for this purpose. Yet, a challenging problem is how to evaluate generated music by a machine. In this paper, a methodology has been developed based upon an interactive evolutionary optimization method, with which the scoring of the generated melodies is primarily performed by human expertise, during the training. This music quality scoring is modeled using a Bi-LSTM recurrent neural network. Moreover, the innovative generated melody through a Genetic algorithm will then be evaluated using this Bi-LSTM network. The results of this mechanism clearly show that the proposed method is able to create pleasurable melodies with desired styles and pieces. This method is also quite fast, compared to the state-of-the-art data-oriented evolutionary systems.