ATK is updated to 3.0.0 with a major ABI break and code quality improvement (see here). Bugs in different areas were fixed.
Development for additional modules was also simplified (the modelling lite is such a project based on Audio Toolkit).
A few year ago, Packt Publishing contacted to be a technical reviewer for the first edition of Building Machine Learning Systems with Python, and I was impressed by the writing of Luis Pedro Coelho and Willi Richert. For the second edition, I was again a technical reviewer.
Writing is not easy, especially when it’s not your mother tongue, and scientific books are plagued with books that are not that great, with low technical content or bad English (that can be said for novels as well, the worst I ever read probably being the Hunger games series…). Even if I don’t like the books, I know that the authors did their best, having written in the past a book that I can say was not very great in terms of flow. Writing a book always deserves the deepest respect.
After my previous post on SPICE modelling in Python, I need to use a good support example to go up to on the fly compilation in C++. This schema will also require some changes to support more than simple nodal analysis, so this now becomes Modified Nodal Analysis with state equations.
A few month ago, mystran published on KVR a small SPICE simulator for real-time processing. I liked the idea, the drawback being that the code is generic and not tailored like a static version of the optimizer. So I wondered if it was doable. But for this, I have to start from the basics and build from there. So let’s go.
ATK is updated to 2.3.0 with major fixes and code coverage improvement (see here). Lots of bugs were fixed during that effort and native build on embedded platforms was also fixed.
CMake builds on Linux don’t have to be installed before Python tests have to be ran. SIMD filters are now also easier to implement.
ATK is updated to 2.2.0 with the major introduction of vectorized filters. This means that some filters (EQ for now) can use vectorization for maximum performance. More filters will be introduced later as well as the Python support. Vector lanes of size 4 and 8 are supported as well as instruction sets from SSE2 to AVX512.
This is also the first major release that officially supports the JUCE framework. This means that ATK can be added as modules (directly source code without requiring any binaries) in the Projucer. The caveat is that SIMD filters are not available in this configuration due to the requirement for CMake support to build the SIMD filters.
ATK is updated to 2.1.0 with a major refactoring of the Python wrappers and extensive testing of them. New filters were also added to support more complex pipelines (mute/solo and circular buffers for real-time spectrum displays) and Audio ToolKit provies now a CMake configuration file for easier integration in CMake projects.
ATK is updated to 2.0.0 with a major refactoring to ensure signed/unsigned consistency, new Adaptive module and EQ design. Complex-valued filters are also now available to allow simultaneous dual channel processes and advanced filters like complex LMS filters.