Evaluation of generative AI is a difficult problem, especially in artistic domains in which aesthetic qualities of generated samples are to an extent subjective, such as in music. The most widely accepted method for evaluating such models is to conduct a survey of users, which is a resource intensive process. In this work we propose a framework for cheaply evaluating generative models in the symbolic music domain by utilizing tools from music theory, such as the circle of fifths, with the goal of producing quantifiable metrics which reflect the "musicality" of a written score or MIDI file.

Digital era has changed the way music is produced and propagated creating new needs for automated and more effective management of music tracks in big volumes. Automated music mood detection constitutes an active task in the field of MIR (Music Information Retrieval) and connected with many research papers in the past few years. In order to approach the task of mood detection, we faced separately the analysis of musical lyrics and the analysis of musical audio signal. Then we applied a uniform multichannel analysis to classify our data in mood classes. The available data we will use to train and evaluate our models consists of a total of 2.000 song titles, classified in four mood classes {happy, angry, sad, relaxed}. The result of this process leads to a uniform prediction for emotional arousal that a music track can cause to a listener and show the way to develop many applications.