Tag Archives: Numpy

Audio Toolkit: ATKCompressor and ATKExpander Implementation

I’d like to talk a little bit about the way a compressor and an expander can be written with the Audio Toolkit. Even if both effects use far less filters than the SD1 emulation, they still implement more than just one filter in the pipeline, contrary to a fixed delay line (another audio plugin that I released with the compressor and the expander).

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Book review: Learning scikit-learn – Machine Learning in Python

It seems that Packt Publishing is on a publishing spree on Machine Learning in Python. After Building Machine Learning Systems In Python for which I was technical reviewer, Packt published Learning Scikit-Learn In Python last November.

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Annoucement: scikits.optimization 0.3

I’m please to announce a new version for scikits.optimization. The main focus of this iteration was to finish usual unconstrained optimization algorithms.

Changelog

  • Fixes on the Simplex state implementation
  • Added several Quasi-Newton steps (BFGS, rank 1 update…)

The scikit can be installed with pip/easy_install or downloaded from PyPI

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Optimization scikit: Polytope (Simplex/Nelder-Mead) optimization

Now that version 0.2 of scikit.optimization is out, here is a tutorial on the gradient-free optimizer based on the simplex algorithm.

When the only thing you have is the cost function and when you don’t have dozens of parameters, the first thing that can be tried is a simplex algorithm.

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Annoucement: scikits.optimization 0.2

It has been a while, too long for sure, since my last update on this scikit. I’m pleased to announce that some algorithms are finally fixed as well as some tests.

Changelog:

  • Fixed Polytope/Simplex/Nelder-Mead
  • Fixed the Quadratic Hessian helper class

Additional tutorials will be available in the next weeks.

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Electronic: The purpose of an oversampling filter

A few months ago, I’ve posted a note on an overdrive. The main issue of this kind of non-linear filter is aliasing, a process that adds digital acoustic content. The best way to solve the issue is to oversample the input before processing the signal.

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