  We define salient features as features that are shared by signals that are
defined as being equivalent by a system designer. The definition allows the
designer to contribute qualitative information. We aim to find salient features
that are useful as conditioning for generative networks. We extract salient
features by jointly training a set of clones of an encoder network. Each
network clone receives as input a different signal from a set of equivalent
signals. The objective function encourages the network clones to map their
input into a set of features that is identical across the clones. It
additionally encourages feature independence and, optionally, reconstruction of
a desired target signal by a decoder. As an application, we train a system that
extracts a time-sequence of feature vectors of speech and uses it as a
conditioning of a WaveNet generative system, facilitating both coding and
enhancement.
