  Many applications of single channel source separation (SCSS) including
automatic speech recognition (ASR), hearing aids etc. require an estimation of
only one source from a mixture of many sources. Treating this special case as a
regular SCSS problem where in all constituent sources are given equal priority
in terms of reconstruction may result in a suboptimal separation performance.
In this paper, we tackle the one source separation problem by suitably
modifying the orthodox SCSS framework and focus only on one source at a time.
The proposed approach is a generic framework that can be applied to any
existing SCSS algorithm, improves performance, and scales well when there are
more than two sources in the mixture unlike most existing SCSS methods.
Additionally, existing SCSS algorithms rely on fine hyper-parameter tuning
hence making them difficult to use in practice. Our framework takes a step
towards automatic tuning of the hyper-parameters thereby making our method
better suited for the mixture to be separated and thus practically more useful.
We test our framework on a neural network based algorithm and the results show
an improved performance in terms of SDR and SAR.
