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Dialog Enhancement (DE) is a feature which allows a user to increase thelevel of dialog in TV or movie content relative to non-dialog sounds. When onlythe original mix is available, DE is "unguided," and requires sourceseparation. In this paper, we describe the DeepSpace system, which performssource separation using both dynamic spatial cues and source cues to supportunguided DE. Its technologies include spatio-level filtering (SLF) anddeep-learning based dialog classification and denoising. Using subjectivelistening tests, we show that DeepSpace demonstrates significantly improvedoverall performance relative to state-of-the-art systems available for testing.We explore the feasibility of using existing automated metrics to evaluateunguided DE systems.

DeepSpace: Dynamic Spatial and Source Cue Based Source Separation for Dialog Enhancement