MDX-Mixer: Music Demixing by Leveraging Source Signals Separated by Existing Demixing Models
Tomoyasu NAKANO,
Masataka GOTO
Abstract:This paper presents MDX-Mixer, which improves music demixing (MDX) performance by leveraging source signals separated by multiple existing MDX models. Deep-learning-based MDX models have improved their separation performances year by year for four kinds of sound sources: "vocals," "drums," "bass," and "other". Our research question is whether mixing (i.e., weighted sum) the signals separated by state-ofthe-art MDX models can obtain either the best of everything or higher separation performance. Previously, in … Show more
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