This is mainly a bug fix release. A nasty bug on increasing processing sizes would corrupt the input data and thus change the results. It is advised to upgrade to this release as soon as possible.
This is the first stable release of the Audio Toolkit, after more than a year of development. In addition to the serial pipeline, there is now an option to use TBB to render each chunk in parallel. The pipeline can also return the maximum latency the pipeline possesses if all latency information is given during the build of the pipeline.
Additional filters were also added to complement the current set of filters.
There are several different low pass filters, and as many high pass, band pass, band stop… filters. In Audio toolkit, there are different usual implementation available:
- Chebyshev type 1
- Chebyshev type 2
- Second order
and it is possible to implement other, different orders as well…
Focus on this release was on performance. As such the core functions were optimized, as well as some tools and EQ.
A new filter dedicated to fast convolution (using a fixed-size partition with a mix of FFT convolution and explicit FIR filter) with 0 latency was added.
When I first read about transient shaper, I was like “what’s the difference with a compressor? Is there one?”. And I tried to see how to get these transient without relying on the transient energy, with a relative power (ratio between the instant power and the mean power) filter, or its derivative, but nothing worked. Until someone explained that the gain was driven by the difference between a fast attack filtered power and a slower one. So here it goes.
The main changes for this release are first trials at modulated filters, C++11 usage (nullptr, override and final), and some API changes (the main process_impl function is now const).
Almost 18 months ago, I posted a small post on the first version of this book (http://blog.audio-tk.com/2013/09/04/book-building-machine-learning-systems-in-python/). At the time, I was really eager to see the second edition of it. And there it is!
I had once again the privilege of being a technical reviewer for this book, and I havce to say that the quality didn’t lower one bit, it went even higher. Of course, there is still room for a better book, when all Python module for Machine Learning are even better. I guess that will be for the third edition!
To get the book from the publisher: https://www.packtpub.com/big-data-and-business-intelligence/building-machine-learning-systems-python-second-edition
On other matters, the blog was quiet for a long time, I’m hoping to get some time to post a few new posts soon, but it is quite hard as I’m currently studying for another master’s degree!