Automatic music generation, particularly accompaniment, poses unique challenges due to the need for responsiveness to other instruments. We present a system that accompanies bass guitar players with AI-generated drum tracks using Conditional Generative Adversarial Networks (CGANs) trained on multi-track songs to capture bass guitar-drum interactions, with our contribution focused on enhancing the expressiveness of the generated drum tracks. To enhance expressiveness and human-like performance, a transformer model trained on human-performed drum recordings assigns velocities (the loudness of each drum strike) to the generated drum tracks. An ablation study was conducted, and the results indicate that combining pitch and velocity generation into a single network significantly reduces music quality (measured by groove consistency), reinforcing our approach of separating velocity assignments to maintain coherent drum patterns while enhancing expressiveness. We also evaluate the generated music using objective metrics, demonstrating the models’ performance and evolution during training. The drum generation system supports real-time interaction, enabling spontaneous live jamming sessions. Simplifications facilitate real-time operation, and we provide results from sample sessions.