  We propose a hierarchical meta-learning-inspired model for music source
separation (Meta-TasNet) in which a generator model is used to predict the
weights of individual extractor models. This enables efficient
parameter-sharing, while still allowing for instrument-specific
parameterization. Meta-TasNet is shown to be more effective than the models
trained independently or in a multi-task setting, and achieve performance
comparable with state-of-the-art methods. In comparison to the latter, our
extractors contain fewer parameters and have faster run-time performance. We
discuss important architectural considerations, and explore the costs and
benefits of this approach.
