Deep Vintage
传奇硬件模拟，蕴含深邃复古灵魂
Deep Vintage 是一套传奇硬件模拟插件，将带您沉浸于真实的模拟魔力之中。Deep Vintage 不仅追求精准的电路复制或特定的音质，更全面地模拟声音，在数字世界中重现复古的"灵魂"。深度、光泽、低频饱和度……硬件音效的每一个细微之处都已准备就绪，等待您尽情释放，同时将 CPU 占用率和延迟降至最低。
APNN 2.0
Deep Vintage 采用 Three-Body Tech 专有的 APNN（音频处理神经网络）2.0 进行训练，专注于声音本身，创造与原始硬件浑然一体的聆听体验。
APNN 2.0 是一个专门用于模拟模拟硬件的神经网络。在训练过程中，APNN 2.0 和硬件将输入相同的音频，APNN 2.0 将学习硬件如何在波形和频谱维度上改变音频。这意味着训练良好的 APNN 2.0 实例可以捕捉其源硬件的动态和音调特性。下图展示了随着训练的进行，APNN 2.0 的波形和频谱响应偏差如何逐渐减小，最终与原始硬件的音色难以区分。查看下方相应的演示，了解 APNN 2.0 如何逐步学习和复制硬件的声音。
APNN 2.0 实例完成训练后，我们会进行严格的人工测试并不断调整，直到整个团队都通过 ABX 测试。这让我们自豪地宣布：
在数字音频领域，没有什么比 Deep Vintage 更接近真实硬件了。
Transformer X
除了电子管，硬件电路中最能决定音色的部件莫过于变压器——尤其是那些带有标志性"铁音"的复古变压器。Transformer X 的设计灵感源自一款采用复古美式变压器的定制"彩盒"，它可以轻松为您的声音增添温暖、饱满和低频的浑厚。
亮点
多重饱和度，多级音染
凭借 APNN 2.0 的强大功能，Deep Vintage 不仅能模拟特定的频率响应或音染，还能模拟所有微妙的"硬件魔力"：动态、空气感、相移、电子管电压下降、变压器的"铁音"等等。无论是微妙的音染、适度的饱和度，还是震撼的音效，其真实的表现力都会让您忘记它是数字的。
独立谐波控制
使用真实硬件时，谐波量固定在给定的旋钮设置上。然而，Deep Vintage 引入了超乎寻常的灵活性，允许独立控制谐波，使其与所有其他音调特性区分开来。这让您能够调节高驱动设置的音效强度，同时保持干净音色的纯净。
低频饱和度
音频变压器的"铁"声——略微增加的低频宽度和饱和度——体现了真实硬件的声音特性。Deep Vintage 不仅准确地捕捉了这种"铁"声，还允许您在变压器版本和无变压器版本之间切换。无论您追求的是浑厚还是清晰的音色，它都能提供卓越的品质。
重采样/上采样
几乎所有音频处理网络都以固定采样率运行，但我们通过优化网络实现了重采样。Deep Vintage 中完全重新设计的重采样算法确保了所有采样率下一致的精度和保真度，使模拟完全不受采样率限制。此外，它支持高达 8 倍的过采样，有效消除混叠问题。
EQ 协同训练
大多数神经网络只能捕捉硬件的离散状态，因此只能提供有限的 EQ 组合。然而，Deep Vintage 独具匠心地通过额外的 EQ 模拟支持完全连续的 EQ 调整。对于带有 EQ 的模型，"协同训练算法"会同时学习硬件原型的饱和特性，并根据电路微调预先建模的 EQ 模块。这让您在享受真实的硬件音效的同时，拥有完全自由的 EQ 调整能力。
磁带抖动/颤动协同训练
抖动/颤动是由于磁带机走带系统的机械不一致而产生的音高变化。抖动指的是较慢、更明显的音高波动，而颤动则是一种较快的速度变化。
与 EQ 协同训练类似，APNN 2.0 使用物理建模的哇音/颤音模拟，并将其与神经网络协同训练。这不仅使神经网络训练的结果听起来更真实，也使建模的哇音/颤音效果更接近原始硬件。
可调底噪
Deep Vintage 系列模拟了硬件固有的底噪，您可以根据需要调整其音量。
"复古 DAW"模拟
此按钮的设计灵感源自
Deep Vintage
Legendary Hardware Simulations with a Deep Vintage Soul
Deep Vintage is a suite of legendary hardware simulation plugins that will immerse you in real analog magic. Not merely pursuing bona fide circuit replication or specific tonal qualities, Deep Vintage simulates the entirety of sound to reproduce the vintage "soul" in a digital world. Depth, sheen, low-end saturation…every nuance of the hardware's sonic spirit is ready for you to ignite, with minimal CPU usage and latency.
APNN 2.0
Trained with Three-Body Tech's proprietary APNN (Audio Processing Neural Network) 2.0, Deep Vintage learns on sound, and sound alone, creating an indistinguishable listening experience from the original hardwares.
APNN 2.0 is a neural network specializes in simulating analog hardwares. During the training process, APNN 2.0 and the hardware will be inputted with the same audio, and APNN 2.0 will learn how the hardware changes the audio in both waveform and spectrum dimensions. This means that a well-trained APNN 2.0 instance can capture both the dynamic and tonal characteristics of its source hardware. The following diagram demonstrates how, as training progresses, APNN 2.0's waveform and spectrum response deviation gradually decrease, eventually becoming indistinguishable from that of the original hardware. Check out the corresponding demos below to hear how APNN 2.0 progressively learns and replicates the sound of the hardware.
After an APNN 2.0 instance completes its training, we conduct rigorous human testing and make adjustments until our entire team fails the ABX test. This allows us to proudly announce:
in the realm of digital audio, nothing comes closer to real hardware than Deep Vintage.
Transformer X
Other than electron tubes, the most tone-defining component in hardware circuits is the transformer—especially vintage transformers with their signature 'iron sound'. Inspired by a custom 'color box' utilizing a vintage US transformer, Transformer X can easily add warmth, body, and low-end girth to your sound.
Highlights
Multiplex Saturation, Multistage Coloration
With the power of APNN 2.0, Deep Vintage simulates not just specific frequency responses or coloration, but all the subtle 'hardware mojos': dynamics, airness, phase shifts, tube voltage sag, transformer's "iron sound," and more. Whether for subtle coloration, moderate saturation, or crushing the entire audio, its authentic performance will make you forget it's digital.
Independent Harmonics Control
With real hardware, the amount of harmonics is fixed at a given knob setting. However, Deep Vintage introduces surreal flexibility by allowing independent control over harmonics, separate from all other tonal characteristics. This lets you dial in the sonic power of high drive settings while maintaining the purity of a clean tone.
Low Frequency Saturation
The 'iron' sound of audio transformers—gently added low-end girth and saturation—epitomizes the sonic character of real hardware. Deep Vintage not only accurately captures this, but also provides you with the ability to toggle this 'iron' sound on or off, allowing you to switch between transformer or transformer-less versions. Whether you're aiming for a thick or clear tone, it always delivers with exceptional quality.
Re-sampling/Up-sampling
Almost all audio processing networks operates in fixed sample rates, but we've made resampling possible by optimizing our networks. The completely redesigned resampling algorithm in Deep Vintage ensures consistent accuracy and fidelity across all sample rates, making the simulation fully sample rate agnostic. Additionally, up to 8x oversampling is supported, effectively eliminating any aliasing issues.
EQ Co-training
Most neural networks can only capture discrete states of the hardware, thus providing only limited EQ combinations. However, Deep Vintage uniquely supports fully continous EQ adjustment through extra EQ simulations. For models with EQ, the "co-training algorithm" simultaneously learns the hardware prototype's saturation characteristics while fine-tuning a pre-modeled EQ module based on the circuit. This allows you to enjoy the authentic hardware sound while having complete freedom over EQ adjustment.
Tape Wow/Flutter Co-training
Wow/Flutter are pitch variations that occur in tape machines due to mechanical inconsistencies in the tape transport system. Wow refers to slower, more noticeable pitch fluctuations, while flutter on the other hand, is a faster form of speed variation.
Just like EQ co-training, APNN 2.0 uses a physically modeled wow/flutter simulation and co-trains it with the neural network. This not only makes the results of neural network training sound more authentic but also brings the modeled wow/flutter effect closer to the original hardware.
Adjustable Noise Floor
The Deep Vintage series simulates the hardware's inherent noise floor, which you can adjust in amount as needed.
"Retro DAW" Simulation
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