Comparision Of Adversarial And Non-Adversarial LSTM Music Generative Models
This work addresses the problem of lack of note variety in algorithmic music composition for applications in creative AI, but it is incremental as it builds on existing GAN and RNN methods.
The study compared adversarial and non-adversarial training of LSTM models for music generation on MIDI data, finding that adversarial training produced music rated as more aesthetically pleasing by human listeners.
Algorithmic music composition is a way of composing musical pieces with minimal to no human intervention. While recurrent neural networks are traditionally applied to many sequence-to-sequence prediction tasks, including successful implementations of music composition, their standard supervised learning approach based on input-to-output mapping leads to a lack of note variety. These models can therefore be seen as potentially unsuitable for tasks such as music generation. Generative adversarial networks learn the generative distribution of data and lead to varied samples. This work implements and compares adversarial and non-adversarial training of recurrent neural network music composers on MIDI data. The resulting music samples are evaluated by human listeners, their preferences recorded. The evaluation indicates that adversarial training produces more aesthetically pleasing music.