  We present a framework to model the perceived quality of audio signals by
combining convolutional architectures, with ideas from classical signal
processing, and describe an approach to enhancing perceived acoustical quality.
We demonstrate the approach by transforming the sound of an inexpensive musical
with degraded sound quality to that of a high-quality musical instrument
without the need for parallel data which is often hard to collect. We adapt the
classical approach of a simple adaptive EQ filtering to the objective criterion
learned by a neural architecture and optimize it to get the signal of our
interest. Since we learn adaptive masks depending on the signal of interest as
opposed to a fixed transformation for all the inputs, we show that shallow
neural architectures can achieve the desired result. A simple constraint on the
objective and the initialization helps us in avoiding adversarial examples,
which otherwise would have produced noisy, unintelligible audio. We believe
that the current framework proposed has enormous applications, in a variety of
problems where one can learn a loss function depending on the problem, using a
neural architecture and optimize it after it has been learned.
