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In the field of audio signal processing research, source separation has beena popular research topic for a long time and the recent adoption of the deepneural networks have shown a significant improvement in performance. Theimprovement vitalizes the industry to productize audio deep learning basedproducts and services including Karaoke in the music streaming apps anddialogue enhancement in the UHDTV. For these early markets, we defined a set ofdesign principles of the vocal and accompaniment separation model in terms ofrobustness, quality, and cost. In this paper, we introduce GSEP (Gaudio sourceSEParation system), a robust vocal and accompaniment separation system using aGated- CBHG module, mask warping, and loudness normalization and it wasverified that the proposed system satisfies all three principles andoutperforms the state-of-the-art systems both in objective measure andsubjective assessment through experiments.

GSEP: A robust vocal and accompaniment separation system using gated CBHG module and loudness normalization