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Operation Destroy Hypocritical Lawsuits Against AI Music Generation Companies

Multimodal AI Music Generation Framework Core Features & Research Areas 1. Multimodal Contextual Processing for Historical and Predictive Music Analysis Integrates text, images, and historical/cultural context to create meaningful compositions. Analyzes how historical, social, and technological changes influenced musical evolution. Frames musical development within specific cultural and historical contexts, mapping human innovation in music to its time, environment, and philosophical foundations. Develops predictive modeling using public domain datasets, identifying trends in how human-made music evolves in response to cultural shifts, and aligning them with Pythagorean tuning principles. Establishes a framework for linking historical human experiences with future musical directions, allowing AI-generated compositions to evolve with societal and technological trends. 2. Pythagorean Tuning as a Foundation for AI-Generated Music Retains Pythagorean tuning as the core of all compositions, ensuring alignment with human neural processing of sound in music. Introduces real-time pitch bending and microtonal adjustments to allow for expressiveness without abandoning harmonic purity. Modifies Pythagorean tuning dynamically for glissando, vibrato, and emotional transitions while ensuring tonal consistency. 3. AI-Driven Song Structuring & Arrangement Intelligence Develops an autonomous system for structuring complete songs, including intro, verse, chorus, bridge, and outro. Implements adaptive learning that refines song arrangements based on historical patterns and user feedback. 4. Deep-Learning-Based Vocal Performance Modeling Enhances vocal synthesis with prosody modeling, vibrato simulation, and pitch contour adaptation. Simulates human articulation, breath control, and phoneme emphasis to create expressive, lifelike AI vocals. Uses historical vocal phrasing patterns to maintain authenticity in generated singing styles. 5. Ethical AI Training & Transparency Trained on public domai
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Adam Edsall
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@mykungfuisbetterwithyours
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Hey, everyone. I opened this outline up for public use however they want, but I am also ensuring its success. I will leave messages here about my brand name of this as there are new developments. Speak freely. ------------ Multimodal AI Music Generation Framework Core...See more

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Adam Edsall
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@mykungfuisbetterwithyours
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Histlyrical.com

Things are about to get hystlyrical. The race is on.

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Adam Edsall
@mykungfuisbetterwithyours
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Get in here, conversation.: https://www.minds.com/newsfeed/1739757902774427648 #ai

And we are going to have values like ethnicity, height, weight... Essentially trying isolate vocal chord size from data available around it....

...eight... Essentially trying isolate vocal chord size from data available around it.. Subscribe to @mykungfuisbetterwithyours on Minds

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Adam Edsall
@mykungfuisbetterwithyours
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If you're seeking extensive public domain music collections online, here are some notable resources: International Music Score Library Project (IMSLP) IMSLP offers over 736,000 scores and 80,700 recordings of more than 226,000 works by 27,400 composers. It's a comprehensive...See more

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Adam Edsall
@mykungfuisbetterwithyours
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So, right now I am finishing up my AI library with ChatGPTo1, because that needs to get done for AdamEdsall.com, Connext Marketing, and Allez Marketing. That is open source under the MIT license, and will be released on github when I am done. And this is another brand that will come out with the others. I hope more follow suite in the Pythagorean tuning, multimodal, public domain sourced training material space.

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Multimodal AI Music Generation Framework Core Features & Research Areas 1. Multimodal Contextual Processing for Historical and Predictive Music Analysis Integrates text, images, and historical/cultural context to create meaningful compositions. Analyzes how historical, social, and technological changes influenced musical evolution. Frames musical development within specific cultural and historical contexts, mapping human innovation in music to its time, environment, and philosophical foundations. Develops predictive modeling using public domain datasets, identifying trends in how human-made music evolves in response to cultural shifts, and aligning them with Pythagorean tuning principles. Establishes a framework for linking historical human experiences with future musical directions, allowing AI-generated compositions to evolve with societal and technological trends. 2. Pythagorean Tuning as a Foundation for AI-Generated Music Retains Pythagorean tuning as the core of all compositions, ensuring alignment with human neural processing of sound in music. Introduces real-time pitch bending and microtonal adjustments to allow for expressiveness without abandoning harmonic purity. Modifies Pythagorean tuning dynamically for glissando, vibrato, and emotional transitions while ensuring tonal consistency. 3. AI-Driven Song Structuring & Arrangement Intelligence Develops an autonomous system for structuring complete songs, including intro, verse, chorus, bridge, and outro. Implements adaptive learning that refines song arrangements based on historical patterns and user feedback. 4. Deep-Learning-Based Vocal Performance Modeling Enhances vocal synthesis with prosody modeling, vibrato simulation, and pitch contour adaptation. Simulates human articulation, breath control, and phoneme emphasis to create expressive, lifelike AI vocals. Uses historical vocal phrasing patterns to maintain authenticity in generated singing styles. 5. Ethical AI Training & Transparency Trained on public domai