Testing Demucs : Music Source Separation in the Waveform Domain

Repository URL : https://github.com/facebookresearch/demucs

Demucs is facebook research repository to separate sound from different musical instruments in a song.Previously it was done by spectrogram based methods.

Demucs uses convolution based Wave-U-Net architecture .The Wave-U-Net is a convolutional neural network applicable to audio source separation tasks, which works directly on the raw audio waveform, presented in this paper.

The Wave-U-Net is an adaptation of the U-Net architecture to the one-dimensional time domain to perform end-to-end audio source separation. Through a series of downsampling and upsampling blocks, which involve 1D convolutions combined with a down-/upsampling process, features are computed on multiple scales/levels of abstraction and time resolution, and combined to make a prediction.

Summary of the network architecture.

Demucs used Machine Learning based approach , Demcus is trained on MUSDB dataset.The musdb18 is a dataset of 150 full lengths music tracks (~10h duration) of different genres along with their isolated drums, bass, vocals and others stems.

All files from the musdb18 dataset are encoded in the Native Instruments stems format (.mp4). It is a multitrack format composed of 5 stereo streams, each one encoded in AAC @256kbps. These signals correspond to:

  • 0 – The mixture,
  • 1 – The drums,
  • 2 – The bass,
  • 3 – The rest of the accompaniment,
  • 4 – The vocals.

For each file, the mixture correspond to the sum of all the signals.


OS Support
Supported operating systems are Windows, Mac OS , though Linux is not mentioned on the official repository but I was able to test it on ubuntu 18.04 and it works perfectly well

Setting up Environment
My Environment while testing
macOS Mojave
Python 3.7.4
Anaconda conda 4.8.0

First we Need to install Python 3.7 in our system then install Anaconda 4.8

continue . . .

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