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Source separation for music is the task of isolating contributions, or stems, from different instruments recorded individually and arranged together to form a song. Such components include voice, bass, drums and any other accompaniments.
In this paper, we present the synthesized Lakh dataset (Slakh) as a new tool for music source separation research.
To reach information at remote locations, we propose to combine dilated convolution with a modified version of gated recurrent units (GRU) called the `Dilated GRU' to form a block.
We examine the mapping functions of neural networks that are based on the denoising autoencoder (DAE) model, and conditioned on the mixture magnitude spectra.
In this paper we study deep learning-based music source separation, and explore using an alternative loss to the standard spectrogram pixel-level L2 loss for model training.
Isolating individual instruments in a musical mixture has a myriad of potential applications, and seems imminently achievable given the levels of performance reached by recent deep learning methods.
Can we perform an end-to-end music source separation with a variable number of sources using a deep learning model?
Music source separation with deep neural networks typically relies only on amplitude features.
In this work, we propose a denoising Auto-encoder with Recurrent skip Connections (ARC).
Deep clustering is the first method to handle general audio separation scenarios with multiple sources of the same type and an arbitrary number of sources, performing impressively in speaker-independent speech separation tasks.