  Recent neural network strategies for source separation attempt to model audio
signals by processing their waveforms directly. Mean squared error (MSE) that
measures the Euclidean distance between waveforms of denoised speech and the
ground-truth speech, has been a natural cost-function for these approaches.
However, MSE is not a perceptually motivated measure and may result in large
perceptual discrepancies. In this paper, we propose and experiment with new
loss functions for end-to-end source separation. These loss functions are
motivated by BSS\_Eval and perceptual metrics like source to distortion ratio
(SDR), source to interference ratio (SIR), source to artifact ratio (SAR) and
short-time objective intelligibility ratio (STOI). This enables the flexibility
to mix and match these loss functions depending upon the requirements of the
task. Subjective listening tests reveal that combinations of the proposed cost
functions help achieve superior separation performance as compared to
stand-alone MSE and SDR costs.
