Correct crossfeed model

This aims to design a filter whose side-channel frequency response is as
flat as possible, and whose cross-channel response matches our desired
transfer function as closely as possible.

That does mean fixing multiple bugs that prevented this model from working
well before. Specifically, the transfer function wasn't being mapped to FFT
bins properly and the error calculation made bad assumptions. Hopefully
that's all fixed now.
Use re-designed crossfeed filters

These weren't made with the designer included in this project. See the
functions at https://gist.github.com/LnxPrgr3/8262666 for the general design
method.

Each filter is an impulse and a delayed crossfeed filter convolved with a
filter designed to minimize changes to overall frequency response.
Re-redesign crossfeed filter!

I've changed the transfer function to reflect measurements I found
[online][^1] and time-reversed the cross channel's impulse response. This
should both better match what happens in real life and be around twice as
fast to execute!

[^1]: http://www.cns.nyu.edu/~david/courses/perception/lecturenotes/localization/localization.html
Promote experimental/return-to-ms

To my ears at least, this design works significantly better, and it does it
in less CPU time too! What's not to love!
Remove mention of 250us delay from README

The filter is now zero-delay.