With the rise of artificial intelligence (AI), there has been increasing interest in human-AI co-creation in a variety of artistic domains including music as AI-driven systems are frequently able to generate human-competitive artifacts. Now, the implications of such systems for the musical practice are being investigated. This paper reports on a thorough evaluation of the user adoption of the Multi-Track Music Machine (MMM) as a minimal co-creative AI tool for music composers. To do this, we integrate MMM into Cubase, a popular Digital Audio Workstation (DAW), by producing a "1-parameter" plugin interface named MMM-Cubase, which enables human-AI co-composition. We conduct a 3-part mixed method study measuring usability, user experience and technology acceptance of the system across two groups of expert-level composers: hobbyists and professionals. Results show positive usability and acceptance scores. Users report experiences of novelty, surprise and ease of use from using the system, and limitations on controllability and predictability of the interface when generating music. Findings indicate no significant difference between the two user groups.

Calliope is a web application for co-creative multi-track music composition (MMM) in the symbolic domain. It is built to facilitate the use of multi-track music machine (MMM). The user can upload Musical Instrument Digital Interface (MIDI) files, visualize and edit MIDI tracks, and generate partial (via bar in-filling) or complete multi-track content using the Multi-Track Music Machine (MMM). Generation of new MIDI excerpts can be done in batch and can be combined with active playback listening for an enhanced Computer-assisted Composition (CAC) workflow. The user can export generated materials as MIDI files or directly stream MIDI playback from the system to their favorite Digital Audio Workstation (DAW). Calliope can be used for creative ideation and for exploring alternatives of musical phrases in composition.

We present and evaluate an application of affective adaptive generative music in a single-player, action-RPG video game. We create a score that serves as an audience to the gameplay, based on the output of PreGLAM, which models the emotional perception of a game audience. We use the Multi-track Music Machine to expand and extend a composed adaptive musical score, and we use industry-standard production techniques to synthesize and perform all of our musical scores. We evaluate our application of generative music in comparison to two composed scores, one adaptive and one linear. Our generative score is rated as nearly equivalent to a composed linear score in perceptions of emotional congruency, immersion, and preference.