A Recurrent Encoder-Decoder Approach with Skip-filtering Connections for Monaural Singing Voice Separation

Understand

The objective of deep learning methods based on encoder-decoder architectures for music source separation is to approximate either ideal time-frequency masks or spectral representations of the target music source(s).

• The spectral representations are then used to derive time-frequency masks.
• In this work we introduce a method to directly learn time-frequency masks from an observed mixture magnitude spectrum.
• We employ recurrent neural networks and train them using prior knowledge only for the magnitude spectrum of the target source.