Audio source separation and speech enhancement are fast evolving fields with a growing number of papers submitted to conferences each year. While datasets such as wsj0-{2, 3}mix, WHAM or MS-SNSD are being shared, there has been little effort to create common codebases for development and evaluation of source separation and speech enhancement algorithms. Hence AsSteroid !

The intent of Asteroid is to be a community-based project, to go beyond sharing datasets. We share tools to create new algorithms as well as recipes to reproduce published papers.

• User friendliness.
• Modularity.
• Extensibility.
• Reproducibility.

• Deep clustering (Hershey et al. and Isik et al.)
• Tasnet (Luo et al.)
• ConvTasnet (Luo et al.)
• Chimera ++ (for ) (Luo et al. and Wang et al.)
• FurcaNeXt (Shi et al.)
• DualPathRNN (Luo et al.)
• Two step learning (Tzinis et al.)

Analysis-synthesis

• STFT (See unit tests)
• Free e.g fully learned (Luo et al.)
• Analytic free e.g fully learned under analycity constraint (Pariente et al.)
• Parametrized Sinc (Pariente et al.)

Analysis only (can be extended)

• Fixed Multi-Phase Gammatones (Ditter et al.)
• Parametrized modulated Gaussian windows (Openreview)
• Parametrized Gammatone (Openreview)
• Parametrized Gammachirp (Openreview)

Make table with all the results per dataset here.