Baby Audio – TAIP 1.0.0 VST, VST3, AAX, AU WIN.OSX x86 x64

Year / Date of Release : 09.2021
Version : 1.0.0
Developer : Baby Audio
Developer site : babyaud
Format : VST, VST3, AAX, AU
Bit depth : 32bit, 64bit
Tablet : present | MORiA
System Requirements : Mac OS 10.7 and up including Catalina, Big Sur and Mac M1. PC Windows 7 and up.
Description : Warm up your sound!
TAIP is our attempt to create the perfect recorder for the DAW era, combining authentic vintage sound with a modern feature set. Its parameters will allow you to customize the correct ribbon coloring for any need.
Tape recordings have a musical quality that digital mixes often lack. TAIP brings this quality to your DAW.
This will add authentic analog warmth to your tracks, without the need to route any sound outside of the DAW.
Tape saturation plugins are not a new concept. However, our emulation method is this: instead of using the traditional DSP, we developed TAIP based on an AI algorithm designed to decode the invisible nuances of analog circuits.
The result is truly accurate tape emulation that is intuitive and creative to use – with some additional features to support a modern workflow. Use TAIP to add some warmth to your tracks, or “control it like you hate it” as an alternative to your distortion plugins.
“AI” is a frequently overused term. But we believe that this is the future of music technology. It just needs to be used sincerely and for a legitimate purpose.
For a hardware emulation project such as TAIP, AI offers an alternative – and in our opinion more correct – approach to the traditional DSP method. Where conventional DSP emulation would entail “guessing” the influence of various analog components and their interdependencies, we can use artificial intelligence / neural networks to accurately decode the sonic qualities that make the tape recorder sound and behave as it does. It does this by feeding various training data, consisting of dry and processed audio, into the algorithm and teaching it to determine the exact characteristics that make the difference. Once these differences are learned by the AI, it can apply them to a new sound.

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