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Ethan Manilow
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Updating first few sections.
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book/intro/open_src_projects.md

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It has become increasingly common for researchers to release code accompanying
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their research papers. In the era of deep learning, the trained models are also
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sometimes released. Here is a non-exhaustive list of some recent open source
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projects.
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projects. We have prioritized open source projects with code and downloadable
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trained models by the original authors of the research papers described.
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We will discuss some of these architectures in more detail in later sections,
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but here we will provide some highlights and links to their Github repositories,
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do a deep dive into all of them. But there's only so much time, so we choose
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`nussl` and `Scaper` for a few reasons:
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- **We believe that the combination of `nussl`+`Scaper` provides a solid foundation
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for all source separation projects.** We hope to convince you of the
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utility of both of these projects as you work through this tutorial.
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- **The combination of `nussl`+`Scaper` provides a solid foundation
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for all source separation projects.** These projects provide solutions for
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networks, data, evaluation, and interaction all in one.
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- **The lessons we will explore in these two projects extend beyond them.**
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There are many common themes and design patterns in this area of research,
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and as we progress you will start to see the themes again and again. We
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believe that `nussl`+`Scaper` is a good way to explore these themes so that
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you can understand _any_ modern source separation system.
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you can understand the structure of modern source separation systems.
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- **We are the primary developers for these projects.** We _really_ understand
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these tools because, well, we built them! That means we are well equipped to

book/intro/src_sep_101.md

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[iZotopes Spire](https://www.izotope.com/en/products/spire-studio.html).
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## A Historical Perspective
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## Next Steps...
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Source Separation is a problem that has been studied for decades. Although we won't
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be able to cover everything in detail, this tutorial will provide a brief overview
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of methods that we think provide a representative demonstration of important concepts and
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provide practical value.
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In general, modern source separation approaches fall into two broad categories: blind
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source separation approaches and data-driven approaches. Blind source separation
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approaches are algorithms that make explicit assumptions about the auditory scene
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upon which they operate
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In the next section we will provide a brief overview of the open source landscape
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before diving into the basics of source separation.
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[^fn1]: Some creative applications might not have such strict demands; when using

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