Sparse, Efficient, and Semantic Mixture Invariant Training: Taming In-the-Wild Unsupervised Sound Separation

Understand

Supervised neural network training has led to significant progress on single-channel sound separation.

• This approach relies on ground truth isolated sources, which precludes scaling to widely available mixture data and limits progress on open-domain tasks.
• The recent mixture invariant training (MixIT) method enables training on in-the-wild data; however, it suffers from two outstanding problems.
• First, it produces models which tend to over-separate, producing more output sources than are present in the input.