  Monoaural audio source separation is a challenging research area in machine
learning. In this area, a mixture containing multiple audio sources is given,
and a model is expected to disentangle the mixture into isolated atomic
sources. In this paper, we first introduce a challenging new dataset for
monoaural source separation called WildMix. WildMix is designed with the goal
of extending the boundaries of source separation beyond what previous datasets
in this area would allow. It contains diverse in-the-wild recordings from 25
different sound classes, combined with each other using arbitrary composition
policies. Source separation often requires modeling long-range dependencies in
both temporal and spectral domains. To this end, we introduce a novel
trasnformer-based model called Spectro-Temporal Transformer (STT). STT utilizes
a specialized encoder, called Spectro-Temporal Encoder (STE). STE highlights
temporal and spectral components of sources within a mixture, using a
self-attention mechanism. It subsequently disentangles them in a hierarchical
manner. In our experiments, STT swiftly outperforms various previous baselines
for monoaural source separation on the challenging WildMix dataset.
