  While the estimation of what sound sources are, when they occur, and from
where they originate has been well-studied, the estimation of how loud these
sound sources are has been often overlooked. Current solutions to this task,
which we refer to as source-specific sound level estimation (SSSLE), suffer
from challenges due to the impracticality of acquiring realistic data and a
lack of robustness to realistic recording conditions. Recently proposed weakly
supervised source separation offer a means of leveraging clip-level source
annotations to train source separation models, which we augment with modified
loss functions to bridge the gap between source separation and SSSLE and to
address the presence of background. We show that our approach improves SSSLE
performance compared to baseline source separation models and provide an
ablation analysis to explore our method's design choices, showing that SSSLE in
practical recording and annotation scenarios is possible.
