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The objective of deep learning methods based on encoder-decoder architecturesfor music source separation is to approximate either ideal time-frequency masksor spectral representations of the target music source(s). The spectralrepresentations are then used to derive time-frequency masks. In this work weintroduce a method to directly learn time-frequency masks from an observedmixture magnitude spectrum. We employ recurrent neural networks and train themusing prior knowledge only for the magnitude spectrum of the target source. Toassess the performance of the proposed method, we focus on the task of singingvoice separation. The results from an objective evaluation show that ourproposed method provides comparable results to deep learning based methodswhich operate over complicated signal representations. Compared to previousmethods that approximate time-frequency masks, our method has increasedperformance of signal to distortion ratio by an average of 3.8 dB.

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