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软件说明：
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.
预热你的声音！TAIP是我们为DAW时代创造完美录音机的尝试，将正宗的复古声音与现代功能集相结合。其参数将允许您根据任何需要自定义正确的功能区颜色。
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.
磁带录音具有数字混音通常缺乏的音乐品质。TAIP将这种品质带给您的DAW。这将为您的音轨添加真实的模拟温暖，而无需将任何声音路由到DAW之外。
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.
磁带饱和插件不是一个新概念。然而，我们的仿真方法是这样的：我们不是使用传统的DSP，而是基于AI算法开发了TAP，该算法旨在解码模拟电路中不可见的细微差别。
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.
其结果是真正准确的磁带模拟，使用起来直观且富有创意，并具有一些额外的功能以支持现代工作流。使用TAIP给你的音轨增加一些温暖，或者“控制它就像你讨厌它一样”，作为你失真插件的替代品。
“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.
“AI”是一个经常被滥用的术语。但我们相信这是音乐技术的未来。它只需要被真诚地用于合法的目的。对于像TAIP这样的硬件仿真项目，AI提供了一种替代传统DSP方法的方法，我们认为这种方法更为正确。
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.
如果传统的DSP仿真需要“猜测”各种模拟组件的影响及其相互依赖性，我们可以使用人工智能/神经网络精确解码使磁带录音机正常工作的音质。
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.
它通过将各种训练数据（包括干燥和处理过的音频）输入到算法中，并对其进行教学以确定产生差异的确切特征来实现这一点。一旦人工智能了解到这些差异，它就可以将它们应用到新的声音中。
