Baby Audio – TAIP 1.0.0 (VST, VST3, AAX, AU) [WIN.OSX x86 x64]

By | December 1, 2022


Year/Date of Issue: 09.2021
Version: 1.0.0
Developer: Baby Audio
Developer site: babyaud
Format: VST, VST3, AAX, AU
Bit depth: 32bit, 64bit
Tabletka: 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 “tape recorder” for the DAW era, combining authentic vintage sound with a modern feature set. Its options will allow you to customize the right ribbon color 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 having to route any audio outside of your DAW.

Tape saturation plugins are not a new concept. However, our emulation method is this: instead of using a traditional DSP, we developed TAIP based on an AI algorithm designed to decipher the invisible nuances of analog circuits.

The result is a truly accurate tape emulation that is intuitive and creative to use – with some added features to enable a modern workflow. Use TAIP to bring some warmth to your tracks, or “manage it like you hate it” as an alternative to your distortion plugins.

“AI” is a term that is often overused. 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 valid – approach to the traditional DSP method. Where conventional DSP emulation would entail “guessing” the influences of various analog components and their interdependencies, we can use artificial intelligence/neural networks to accurately decipher the sonic qualities that make a tape recorder sound and behave the way it does. This is done by feeding the algorithm various training data, consisting of dry and processed audio, and teaching it to determine the exact characteristics that make up the difference. Once these differences have been learned by the AI, it can apply them to a new sound.

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