  An ideal music synthesizer should be both interactive and expressive,
generating high-fidelity audio in realtime for arbitrary combinations of
instruments and notes. Recent neural synthesizers have exhibited a tradeoff
between domain-specific models that offer detailed control of only specific
instruments, or raw waveform models that can train on any music but with
minimal control and slow generation. In this work, we focus on a middle ground
of neural synthesizers that can generate audio from MIDI sequences with
arbitrary combinations of instruments in realtime. This enables training on a
wide range of transcription datasets with a single model, which in turn offers
note-level control of composition and instrumentation across a wide range of
instruments. We use a simple two-stage process: MIDI to spectrograms with an
encoder-decoder Transformer, then spectrograms to audio with a generative
adversarial network (GAN) spectrogram inverter. We compare training the decoder
as an autoregressive model and as a Denoising Diffusion Probabilistic Model
(DDPM) and find that the DDPM approach is superior both qualitatively and as
measured by audio reconstruction and Fr\'echet distance metrics. Given the
interactivity and generality of this approach, we find this to be a promising
first step towards interactive and expressive neural synthesis for arbitrary
combinations of instruments and notes.
