Music Generation with Temporal Structure Augmentation

Understand

In this paper we introduce a novel feature augmentation approach for generating structured musical compositions comprising melodies and harmonies.

• The proposed method augments a connectionist generation model with count-down to song conclusion and meter markers as extra input features to study whether neural networks can learn to produce more aesthetically pleasing and structured musical output as a consequence of augmenting the input data with structural features.
• An RNN architecture with LSTM cells is trained on the Nottingham folk music dataset in a supervised sequence learning setup, following a Music Language Modelling approach, and then applied to generation of harmonies and melodies.
• Our experiments show an improved prediction performance for both types of annotation.