Three–Body Tech DV TransformerX v1.0.3

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 Shelf
Inspired by one of the most iconic and musical program EQs of all time, Tube Shelf faithfully emulates the EQ characteristics and unique tonal coloration of the original tube electronics.
The Tube Shelf module is primarily used to adjust low and high frequencies. With relatively few frequency band options, it is often paired with other EQs for more detailed tonal shaping, most commonly with Tube Bell and Tube Filter modules (see below).
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
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
Home Page -

https://www.threebodytech.com/en/products/deepvintage