  Deep learning models are typically evaluated to measure and compare their
performance on a given task. The metrics that are commonly used to evaluate
these models are standard metrics that are used for different tasks. In the
field of music composition or generation, the standard metrics used in other
fields have no clear meaning in terms of music theory. In this paper, we
propose a subjective method to evaluate AI-based music composition systems by
asking questions related to basic music principles to different levels of users
based on their musical experience and knowledge. We use this method to compare
state-of-the-art models for music composition with deep learning. We give the
results of this evaluation method and we compare the responses of each user
level for each evaluated model.
