- 产品视频
深度复古
传奇硬件模拟,赋予深邃复古灵魂
Deep Vintage 是一套传奇硬件模拟插件套装,带你沉浸于真实的模拟魔力中。它不仅仅追求真实的电路复制或特定的音色特质,Deep Vintage 模拟了整个声音,以在数字世界中再现复古的“灵魂”。深度、光泽、低频饱和感……硬件音频灵魂的每个细微之处,随时待你激发,并且占用极少的 CPU 资源和低延迟。
APNN 2.0
Deep Vintage 采用由 Three-Body Tech 独有的 APNN(音频处理神经网络)2.0 训练而成,它只依赖于声音进行学习,创造出与原始硬件无可区分的听觉体验。
APNN 2.0 是一种专门用于模拟模拟硬件的神经网络。在训练过程中,APNN 2.0 和硬件会同时输入相同的音频信号,APNN 2.0 学习硬件在波形和频谱维度上对音频的改变。这意味着经过良好训练的 APNN 2.0 实例可以捕捉到硬件源设备的动态和音色特征。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逐渐减少,最终与原始硬件无异。可以查看以下演示,听听 APNN 2.0 如何逐步学习并复制硬件的声音。
当一个 APNN 2.0 实例完成训练后,我们进行严格的人工测试,并做出调整,直到团队中的每个人都在 ABX 测试中无法分辨出差异。正因如此,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。
Tube Bell
作为 Tube Shelf 的补充,Tube Bell 主要负责中频处理。它具有两个峰值段和一个凹陷段,全部采用 Bell 型滤波器。
亮点
多级饱和,多重色彩
借助 APNN 2.0 的强大功能,Deep Vintage 不仅模拟了特定的频率响应或色彩,而是所有微妙的“硬件灵性”:动态感、空气感、相移、电子管电压衰减、变压器的“铁声”等。不论是微妙的着色、中度饱和还是压制整个音频,它的真实表现让你忘记它是数字的。
独立谐波控制
在真实硬件中,谐波量在给定的旋钮设置下是固定的。然而,Deep Vintage 提供了超现实的灵活性,允许独立控制谐波,与其他音色特性分离。这让你可以调节高驱动设置的声音力量,同时保持纯净的音色。
低频饱和
音频变压器的“铁声”——轻微增加的低频厚度和饱和感——体现了真实硬件的音色特征。Deep Vintage 不仅准确捕捉了这一点,还提供了开启或关闭这种“铁声”的能力,让你在变压器或无变压器版本之间切换。无论你追求厚重或清晰的音色,它总能以卓越的品质呈现。
重新采样/超采样
几乎所有的音频处理网络都在固定采样率下运行,但我们通过优化网络实现了重新采样。Deep Vintage 全新设计的重新采样算法确保了在所有采样率下的一致精度和保真度,使模拟完全与采样率无关。此外,还支持最高 8 倍超采样,有效消除任何混叠问题。
EQ 联合训练
大多数神经网络只能捕捉硬件的离散状态,从而提供有限的 EQ 组合。而 Deep Vintage 通过额外的 EQ 模拟独特地支持完全连续的 EQ 调整。对于带有 EQ 的模型,“联合训练算法”同时学习硬件原型的饱和特性,并基于电路微调预建的 EQ 模块。这让你既能享受真实硬件的声音,又能自由调整 EQ。
磁带 Wow/Flutter 联合训练
Wow/Flutter 是由于磁带机传输系统的机械不一致性而引起的音高变化。Wow 是指较慢、更明显的音高波动,而 Flutter 则是较快的速度变化。
与 EQ 联合训练类似,APNN 2.0 使用物理建模的 Wow/Flutter 模拟,并与神经网络共同训练。这不仅使神经网络训练的结果更为真实,还让建模的 Wow/Flutter 效果更接近原始硬件。
可调噪声底
Deep Vintage 系列模拟了硬件固有的噪声底,并允许根据需要调整其量。
“复古 DAW”模拟
这个按钮灵感来自于 2000 年左右的经典 DAW 引入的极其微妙的变化(小于 -140 dB)。虽然这些变化极其细微,但我们也没有忽略它。你可以根据需要启用或禁用此功能。
低 CPU 占用
无需昂贵的基于云的 GPU 集群——Deep Vintage 就像其他插件一样,本地运行,占用极少的 CPU 资源。你可以轻松地在每条轨道上插入它!
更多功能
- 原生支持 Apple Silicon
- 撤销/重做
- A/B 切换
- 输入/输出电平表
- 单声道模式
- LR/MS 处理
- 相位反转
- GUI 界面缩放
延迟
26 个采样点,约 0.6 毫秒(44100 Hz 下)
Rev1 修复计时炸弹问题
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.Tube Bell
As a complement to Tube Shelf, Tube Bell is primarily responsible for mid-frequency processing. It features two Peak sections and a Dip section, all utilizing Bell-type filters.
HighlightsMultiplex 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
This button is inspired by the extremely subtle changes (less than -140 dB) introduced by a classic DAW from around the year 2000. While the changes are infinitesimal, we didn’t overlook them. You can enable or disable this feature as needed.
Low CPU usage
No need for expensive cloud-based GPU clusters – Deep Vintage runs locally just like any other plugin, with extremely low CPU usage. You can easily insert it on every track!
More Features
- Apple silicon native support
- Undo/redo
- A/B switching
- Input/output level meter
- Mono mode
- LR/MS processing
- Phase invert
- GUI re-scaling
Latency
26 sample points, about 0.6ms at 44100 Hz
Rev1 Fix timebomb.