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深度复古
AI驱动的硬件模拟插件系列

这是一台致力于通过人耳图灵测试的机器，
一种拥有复古灵魂的深度学习AI技术。

Deep Vintage系列是一套模拟真实硬件的插件套件。利用AI的强大功能，每个Deep Vintage插件都能真实捕捉复古设备的“灵魂”。

由Three-Body Tech自主研发的APNN（音频处理神经网络）驱动，这是一种专门用于模拟模拟效果处理器的机器学习技术。Deep Vintage为您提供前所未有的接近传奇硬件模型的听觉体验，同时确保实现实时处理。

它有什么不同？ 我们已经接触过许多仿真技术：物理建模、卷积……还有许多其他方法。无论采用何种技术，我们的最终目标始终如一：在重现真实设备声音时，达到最高的保真度。

APNN经过精心设计和训练，专门用于这一任务，因为它的学习基于真实硬件设备的音频信号。Deep Vintage插件不仅仅是“理论上正确的电路建模”，也不仅基于谐波结构或脉冲响应；它捕捉到了模拟声音的100% 3D质感。

它是如何工作的？ 简单来说，APNN是一个专为音频处理优化的神经网络，由大量音频处理模块（如均衡器、压缩器、过载等）组成。

为了捕捉硬件模型的精髓，APNN会自动调整其结构和参数，直到其输出与硬件的输出之间的差异逐渐减小。

最终，APNN可以实现约-40dB到-75dB的相位抵消误差信号（具体取决于硬件模型）。凭借这种出色的误差控制水平，甚至超过了同一硬件模型不同生产批次之间的差异，我们可以自信地说，APNN足以“欺骗”人类的耳朵。

Brit 73 AI
灵感来自这个星球上最传奇的前置放大器。这款A类晶体管前置放大器带有均衡器，完美展现了声音的美感，提供无与伦比的清晰度、光泽和锐度。

[引用] 首先，感谢所有决定帮助我的人！我永远感激不尽！你们无法想象你们选择帮助一个网络上的陌生人对我来说有多重要。

有人问我为什么选择在评论中隐藏信息。答案很简单，我不想让这件事影响发布，我仍然觉得向别人寻求帮助有些奇怪。

正如在Tone Projects发布中所提到的，这将是我一段时间内的最后一次更新。我会尝试优先处理一些其他事情，整理好自己。

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Deep Vintage

AI-Powered Hardware Simulation Plugin Series

This is a machine that endeavors to pass the human-ear Turing Test,

a deep-learning AI technology with a vintage heart.

Deep Vintage series is a suite of plugins that simulate real hardware. Utilizing the power of AI, every Deep Vintage plugin authentically captures the true ‘soul’ of the vintage gear.

Powered by Three-Body Tech’s self-developed APNN (Audio Processing Neural Network), a machine learning technology specialized in simulating analog effect processors, Deep Vintage guarantees you the closest listening experience ever to the legendary hardware models, while ensuring a complete real-time processing.

How is it different?

We have been exposed to numerous emulation technologies: physical modeling, convolution… among many others. Regardless of what technics to engage, our ultimate goal is consistent: to achieve the highest possible fidelity in reproducing the sound of the real-world devices.

APNN was meticulously designed and trained to excel in this task, as its learning is based on audio signals from the real hardware units. Deep Vintage plugins are not merely ‘theoretically correct modeling of circuits’ nor solely based on harmonic structures or impulse responses; they capture 100% 3D feels in the analog sound.

How does it work?

To put it simply, APNN is a neural network specifically optimized for audio processing, which is composed of a massive combination of audio processing modules (EQ, compressor, overdrive, etc.).

To capture the essence of a hardware model, APNN will automatically adjust its structure and parameters until the difference between its output and the hardware’s output progressively diminishes.

Ultimately, APNN achieves an error signal of phase cancellation at about -40dB to -75dB (depending on the hardware model). With this exceptional level of error control, which surpasses even the variance between different production batches of the same hardware model, we can confidently say that APNN is capable of ‘deceiving’ human ears.

Brit 73 AI

Inspired by the most legendary preamp on this planet. This class-A, transistor preamp with EQ epitomizes the beauty of sound, offering unparalleled clarity, sheen, and bite.

[quote]Firstly, let me saythank you to everyone who decided to help me out! I am forever grateful! You have no idea how much it means that you chose to help some rando on the internet.

Someone asked why I decided to hide my message in the comments. The answer is simply because I did not want that to detract from the release and I still feel weird about asking for help in the first place.

As stated in the Tone Projects release, this will be it for me for a while. I’m gonna try and prioritize some other things and get myself sorted out.

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