In this paper, we introduce means of improving fidelity and controllability of the RAVE generative audio model by factorizing pitch and other features. We accomplish this primarily by creating a multi-band excitation signal capturing pitch and/or loudness information, and by using it to FiLM-condition the RAVE generator. To further improve fidelity when applied to a singing voice application explored here, we also consider concatenating a supervised phonetic encoding to its latent representation. An ablation analysis highlights the improved performance of our incremental improvements relative to the baseline RAVE model. As our primary enhancement involves adding a stable pitch conditioning mechanism into the RAVE model, we simply call our method P-RAVE.