  In the field of audio signal processing research, source separation has been
a popular research topic for a long time and the recent adoption of the deep
neural networks have shown a significant improvement in performance. The
improvement vitalizes the industry to productize audio deep learning based
products and services including Karaoke in the music streaming apps and
dialogue enhancement in the UHDTV. For these early markets, we defined a set of
design principles of the vocal and accompaniment separation model in terms of
robustness, quality, and cost. In this paper, we introduce GSEP (Gaudio source
SEParation system), a robust vocal and accompaniment separation system using a
Gated- CBHG module, mask warping, and loudness normalization and it was
verified that the proposed system satisfies all three principles and
outperforms the state-of-the-art systems both in objective measure and
subjective assessment through experiments.
