  Despite the recent success of speech separation models, they fail to separate
sources properly while facing different sets of people or noisy environments.
To tackle this problem, we proposed to apply meta-learning to the speech
separation task. We aimed to find a meta-initialization model, which can
quickly adapt to new speakers by seeing only one mixture generated by those
people. In this paper, we use model-agnostic meta-learning(MAML) algorithm and
almost no inner loop(ANIL) algorithm in Conv-TasNet to achieve this goal. The
experiment results show that our model can adapt not only to a new set of
speakers but also noisy environments. Furthermore, we found out that the
encoder and decoder serve as the feature-reuse layers, while the separator is
the task-specific module.
