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This branch of Asteroid contains code for the ensemble separation related papers below:

• [Choral Music Separation using Time-domain source separation] (./egs/MedleyDB/ConvTasNet) (Sarkar et al.)
• [EnsembleSet: A new high-quality synthesised dataset for chamber ensemble separation] (./asteroid/data/bbcso_dataset.py) (Sarkar et al.)
• [Leveraging Synthetic Data for Improving Chamber Ensemble Separation] (/egs/BBCSO/DPTNet) (Sarkar et al.)

This repo is forked from Asteroid, which is a Pytorch-based audio source separation toolkit that enables fast experimentation on common datasets. It comes with a source code that supports a large range of datasets and architectures, and a set of recipes to reproduce some important papers.

Contents

• Installation
• Running a recipe
• Available recipes
• Supported datasets
• Citing us

Installation

(↑up to contents) To install Asteroid, clone the repo and install it using conda, pip or python :

# First clone and enter the repo
git clone https://github.com/saurjya/EnsembleSep
cd EnsembleSep

• With pip

# Install with pip in editable mode
pip install -e .
# Or, install with python in dev mode
# python setup.py develop

• With conda (if you don't already have conda, see [here][miniconda].)

conda env create -f environment.yml
conda activate asteroid

• Asteroid is also on PyPI, you can install the latest release with

pip install asteroid

Running a recipe

(↑up to contents) Running the recipes requires additional packages in most cases, we recommend running :

# from asteroid/
pip install -r requirements.txt

Then choose the recipe you want to run and run it!

cd egs/BBCSO/DPTNet
. ./run.sh

More information in egs/README.md.

Available recipes

(↑up to contents)

• ConvTasnet (Luo et al.)
• DualPathRNN (Luo et al.)
• DPTNet (Chen et al.)
• DCUNet (Choi et al.)

Supported datasets

(↑up to contents)

• EnsembleSet / BBCSO (Sarkar et al.)