  We present a deep learning based methodology for extracting the singing voice
signal from a musical mixture based on the underlying linguistic content. Our
model follows an encoder decoder architecture and takes as input the magnitude
component of the spectrogram of a musical mixture with vocals. The encoder part
of the model is trained via knowledge distillation using a teacher network to
learn a content embedding, which is decoded to generate the corresponding
vocoder features. Using this methodology, we are able to extract the
unprocessed raw vocal signal from the mixture even for a processed mixture
dataset with singers not seen during training. While the nature of our system
makes it incongruous with traditional objective evaluation metrics, we use
subjective evaluation via listening tests to compare the methodology to
state-of-the-art deep learning based source separation algorithms. We also
provide sound examples and source code for reproducibility.
