Voxengo TEOTE automatic spectral balancer plugin released

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TEOTE$55.00Buy

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October 16, 2020: Voxengo is very happy to release TEOTE, an automatic spectral balancer plugin for professional music production applications, available in AAX, AudioUnit, VST, and VST3 plugin formats, for macOS and Windows computers. TEOTE was designed to be a very useful tool for both mixing and mastering. It automatically performs such tasks like resonances removal, de-essing, tilt equalizing, usually performed during mixing and mastering. In mixing, TEOTE sounds good on pretty much any material.

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While by definition TEOTE is a dynamic equalizer, its technology is solely based on multi-band dynamics processing. This allows TEOTE to have only minor phase issues, and to produce a subtle transient-emphasis effect associated with dynamics processing. TEOTE tries to make the program material follow the required spectral profile, tuned to the contemporary mastering standards by default.

Is TEOTE an AI plug-in? In a sense that AI usually boils down to “curve-fitting task”, TEOTE is an AI plug-in that performs gain adjustment decisions in a quantity equal to “SampleRate multiplied by BandCount” per second. However, TEOTE does not use neural networks; it is based on an extremely-refined, completely predictable, curve-fitting function.

TEOTE is an acronym for “That’s Easier On The Ear”. TEOTE is a serious contender in helping bring your music production to the next level!

TEOTE features:

* Automatic spectral balancing
* Selectable processing band count
* Unlinked stereo processing
* Multi-band gain adjustments meter
* Stereo and multi-channel processing
* Internal channel routing
* Channel grouping
* Up to 8x oversampling
* 64-bit floating point processing
* Preset manager
* Undo/redo history
* A/B comparisons
* Contextual hint messages
* All sample rates support
* Zero processing latency

Introductory price of US$69.95 is now in effect until October 30, 2020 (regular US$79.95). TEOTE is free for Voxengo Premium Membership owners. Demo version of TEOTE can be downloaded freely at the Voxengo web site: https://www.voxengo.com/product/teote/?eref=fo
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Very cool Aleksey! Thanks for sharing :)
EnergyXT3 - LMMS - FL Studio | Roland SH201 - Waldorf Rocket | SoundCloud - Bandcamp

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Welp, one more Voxengo plugin to get. Looks superb.

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Cool. :)
This is the same method MJ used when he was working on Anthony Marinelli's Thriller.

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Also great price! Intro and regular i mean :)
EnergyXT3 - LMMS - FL Studio | Roland SH201 - Waldorf Rocket | SoundCloud - Bandcamp

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Is this like a better version of Soniformer?

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marzelli wrote: Fri Oct 16, 2020 12:26 pm Is this like a better version of Soniformer?
Soniformer is not automatic the way TEOTE is.
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This will be interesting to compare with gullfoss and/or DSEQ

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free for premium users. God damn, thanks Aleksey :)

demoing now. so far, gullfoss has been my go-to for such shenanigans, can't wait to give it a run for its money.
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The last Unicorn that might make Sense for me these Days. See you on BF.

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I haven't had much of a chance to play with this yet, so bear with me please. :)

When you say: Applications/Contemporary music mastering ... does this mean the plugin follows a pink noise curve or something else? What settings should be used to follow a pink noise curve? Can we create profiles?
This is the same method MJ used when he was working on Anthony Marinelli's Thriller.

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Aloysius wrote: Sat Oct 17, 2020 11:32 am I haven't had much of a chance to play with this yet, so bear with me please. :)

When you say: Applications/Contemporary music mastering ... does this mean the plugin follows a pink noise curve or something else? What settings should be used to follow a pink noise curve? Can we create profiles?
Slope -3 apparently nudges it towards pink noise :)
here's a quick capture with pink noise as source, feeding Gullfoss and TEOTE with default slope and -3 slope.
https://imgur.com/a/uKukS6x
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Wow! Thank you! :D
This is the same method MJ used when he was working on Anthony Marinelli's Thriller.

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On a side note, I would love to see Aleksey's take on bitcrushing, I'm surprised he hasn't come up with a dedicated bit crusher yet.

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Since you don’t use deep learning, how are you learning the function from input?

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