Sage Audio Sage Velvet v1.0.5 macOS

[OS9999]

Free Download Sage Audio Sage Velvet v1.0.5 macOS | 163.95 MB
A Near 1:1 Recreation of a Modern Classic

Sage Velvet may be the first plugin to fully capture the nuances of Analog Equipment. Full of detail and nonlinearities, Sage Velvet is a near-perfect recreation of the hardware that inspired it.
Your Mix Bus has a New Best Friend!
Sage Velvet achieves truly accurate analog emulation. Every detail, nuance, and nonlinearity has been captured with machine learning to create an experience true to the original hardware.
Red & Blue!
Both Red and Blue circuits have been carefully modeled - with Sage Velvet, you'll achieve the full spectrum of modern transformer-based saturation.
The new drive function can push the saturation further than previously possible with the hardware.
Quality of Life Improvements!
In addition to the drive function, a new Emphasis/De-Emphasis filter opens up a new world of harmonic formations and timbres. With auto-compensated Frequency, Gain, and Bandwidth, this may be the best way to introduce the classic emphasis-deemphasis technique.
Master the Stereo Image!
Sage Velvet's simple but effective image controller provides quick control over the width.
The internal HPF excludes processing to the range below the selected frequency - meaning you can achieve an impressive image while keeping your kick and bass driving.
Important Note
Sage Velvet is Intended for One to Two Instances per Session
Sage Velvet utilizes dual-channel neural networks to faithfully recreate the original hardware. This ensures extremely accurate emulation far beyond that of traditional plugin emulations; however, this requires high CPU.
Sage Velvet pushes the envelope of what's possible 'in-the-box', but may be taxing on older systems.
If you're unsure whether your computer can run an instance of Sage Velvet while seamlessly running your session, please try the 14-day trial before making a purchase.
For the best sonic results, use a 48kHz sampling rate to match the sampling rate of the neural models.
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