17.06.2025 @ 15:39 Windows, Mac OSX

Neutone Morpho (Ai Tone Morphing) v1.1.3 VST3 AU STANDALONE WiN MAC [FREE]

17/06/2025 | VST3 AU STANDALONE WiN MAC | 468 MB
Introducing Neutone Morpho - AI Tone Morphing plugin

We’re pleased to present Neutone Morpho, a realtime tone morphing plugin. Our cutting-edge machine learning technology can transform any sound into something new and inspiring. Neutone Morpho directly processes audio, capturing even the subtlest details from your input. Unleash your creativity with Neutone Morpho!

What is AI Tone Morphing?

With pre-trained AI models you can transform any incoming audio into the characteristics, or "style", of the sounds that the model is based on. In real-time. Sometimes this leads to surprising outcomes.

Morpho Model
At the core of Neutone Morpho are the Morpho AI models, where the magic happens. You can interact with a loaded Morpho model in two modes to influence the tone-morphing process.

In Macro Mode, you have four macro knobs that allow you to change the general behaviors of the model. For example, the "Serendipity" knob determines the level of randomness in the transfer, with higher serendipity resulting in more complex sound. Each model has its own unique macros. For instance, a percussion model may have a knob called "decay" that controls the decay of each hit.

The Morpho AI model crunches incoming audio into 6 or more values, referred to as "latent variables." In Micro View, you can directly offset and scale those values with Morpho Modifiers to steer the output in certain directions. You may even uncover a hidden correlation between a latent variable and the pitch of the output.

The Neutone Morpho Algorithm utilizes an autoencoder AI algorithm consisting of an encoder and decoder to transform audio. The encoder analyzes the incoming audio, outputting information about the pitch, loudness and other more subtle features of the incoming audio (this is sometimes referred to as the latent variable). The model is trained on specific types of sounds, such as violin performances, and can create a tone transfer effect by interpreting different input sounds in the context of the trained data.

Requirements