  Despite advances in deep algorithmic music generation, evaluation of
generated samples often relies on human evaluation, which is subjective and
costly. We focus on designing a homogeneous, objective framework for evaluating
samples of algorithmically generated music. Any engineered measures to evaluate
generated music typically attempt to define the samples' musicality, but do not
capture qualities of music such as theme or mood. We do not seek to assess the
musical merit of generated music, but instead explore whether generated samples
contain meaningful information pertaining to emotion or mood/theme. We achieve
this by measuring the change in predictive performance of a music mood/theme
classifier after augmenting its training data with generated samples. We
analyse music samples generated by three models -- SampleRNN, Jukebox, and DDSP
-- and employ a homogeneous framework across all methods to allow for objective
comparison. This is the first attempt at augmenting a music genre
classification dataset with conditionally generated music. We investigate the
classification performance improvement using deep music generation and the
ability of the generators to make emotional music by using an additional,
emotion annotation of the dataset. Finally, we use a classifier trained on real
data to evaluate the label validity of class-conditionally generated samples.
