Matthieu Brucher's blog

My last blog post on optimization helped me generate orthogonal sequences. Now, I will use those sequences to separate two signals. The basic use case is a linear system with two inputs, one output, and instead of recording the response of one input at a time, one plays both inputs simultaneously with specific sequences so that they can be separated in another process.
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When twenty or so langage creators are put together to make a book, it can only be interesting. It’s a good revealer of character, as they tend to open their heart. In fact I think that’s exactly what happened in this book.
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Although I’m fond of numerical optimization through gradients, … there are some times where a global optimization is much more powerfull. For instance, I have to generate two sequences/combs that are orthogonal and for which their autocorrelation is almost an impulse. The two combs have a fixed number of impulse, so it’s a perfect job for genetic algorithms.
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It’s been a while since I last blogged about manifold learning. I don’t think I’ll add much in terms of algorithms to the scikit, but now that a clear API is being defined (http://sourceforge.net/apps/trac/scikit-learn/wiki/ApiDiscussion), it’s time for the manifold module to comply to it. Also, documentation will be enhanced and some dependencies will be removed.

I’ve started a branch available on github.com, and I will some examples in the scikit as well. I may explain them here, but I won’t rewrite what is already published. A future post will explain the changes, and I hope that interested people will understand the modifications and apply them to my former posts. It’s just that I don’t have much time to change everything…

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In my last post about optimization, I’ve derived my function analytically. Sometimes, it’s not as easy. Sometimes also, a simple gradient optimization is not enough.

scikits.optimization has a special class for handling numerical differentiation, and several tools for conjugate gradients.
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Last time, I’ve made a simple example of a gradient-free optimization. Now, I’d like to use the gradient of my function (analytical gradient I’ve computed) to be able to get the global minimum in less iterations.
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I am pleased to announce the first release of PyVST.
PyVST is a ctypes-based wrapper for the (open) VST standard developed by Steinberg for audio processing.

Version 0.1 provides basic access to the VST interface, as well as a script to analyze and display the audio process of a plugin. It can be easy-installed or downloaded on its Launchpad page.

Changelog for 0.1:

• Uses the VST dispatcher for several functions:
  • processReplacing
  • processDoubleReplacing
  • open/close the plugin
  • open/close the GUI editor
  • returns the GUI rectangle
  • set the sample rate
  • set the block size
  • get name/vendor/product
  • handle programs
  • handle parameters
  • suspend/resume
• set/get a parameter
• get number of programs
• get number of inputs
• get number of outputs
• display.py script
  • can load any plugin
  • displays the editor, if it exists
  • uses a stereo sine-sweep
  • displays a spectrogram of the process of the stereo sine-sweep
  • dumps properties information

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In a previous post, I’ve tried to use Qt for the editor window of a VST plugin. The thing is, I want to do more than just play with a GUI, I also want to see what is done to an audio stream by a plugin.

To do so, I’ve decided to expose the VST interface to Python. There are some implementation I’ve heard of, but they are based on Cython or other wrapping tools. Ctypes has the advantage of not needing a compilation step. There are also every functionality needed, as callback creation (plugins use a callback to ask the host some stuffs), and Python provides the additional mathematical tools to display what the plugin does. It may not be perfect, but it will be enough for a starter.
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Some months ago, I’ve finished my manifold learning posts serie. As support for the manifold learning toolkit, I’ve also developed an optimization framework, which I’ll be blogging about, starting now.
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Python can be used for many things, and is mainly known for the shell scripts people wrote. Shai Vangast proposes using the langage for data analysis and visualization.
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