  We compare standard autoencoder topologies' performances for timbre
generation. We demonstrate how different activation functions used in the
autoencoder's bottleneck distributes a training corpus's embedding. We show
that the choice of sigmoid activation in the bottleneck produces a more bounded
and uniformly distributed embedding than a leaky rectified linear unit
activation. We propose a one-hot encoded chroma feature vector for use in both
input augmentation and latent space conditioning. We measure the performance of
these networks, and characterize the latent embeddings that arise from the use
of this chroma conditioning vector. An open source, real-time timbre synthesis
algorithm in Python is outlined and shared.
