This paper proposes several improvements for music separation with deep neural networks (DNNs), namely a multi-domain loss (MDL) and two combination schemes.
Ranked #10 on Music Source Separation on MUSDB18
We present and release a new tool for music source separation with pre-trained models called Spleeter. Spleeter was designed with ease of use, separation performance and speed in mind.
Ranked #8 on Music Source Separation on MUSDB18 (using extra training data)
In this paper, we claim the importance of a dense simultaneous modeling of multiresolution representation and propose a novel CNN architecture called densely connected multidilated DenseNet (D3Net).
In this paper, we claim the importance of a rapid growth of a receptive field and a simultaneous modeling of multi-resolution data in a single convolution layer, and propose a novel CNN architecture called densely connected dilated DenseNet (D3Net).
Ranked #2 on Music Source Separation on MUSDB18 (using extra training data)
Recent deep-learning approaches have shown that Frequency Transformation (FT) blocks can significantly improve spectrogram-based single-source separation models by capturing frequency patterns.
Ranked #9 on Music Source Separation on MUSDB18
In this work we present a method for unsupervised learning of audio representations, focused on the task of singing voice separation.
Sound Audio and Speech Processing
In recent years, deep learning has surpassed traditional approaches to the problem of singing voice separation.
Convolutional Neural Network (CNN) or Long short-term memory (LSTM) based models with the input of spectrogram or waveforms are commonly used for deep learning based audio source separation.