AI AUDIO POST-PROD PIPELINE

Autonomous "Uncanny Valley" Remediator & Dynamic Spectral Mixing Framework

Stage I: Source De-Aggregation
🧬 High-Fidelity Source Separation

Dismantles flat generative audio into discrete instrumental/vocal stems using hybrid transformer architectures.

Demucs v4 (htdemucs_ft) PyTorch CUDA BiLSTM/Transformer
Metric (SDR)8.4 dB (Vocals)
DomainTime + Frequency Hybrid
Hardware8GB+ VRAM Required
Strategy2s Overlapping Chunking
Stage II: Neural Restoration
⚡ Denoising & Bandwidth Extension

Remediates metallic artifacts (8-12kHz) and "spectral holes" via non-stationary neural filtering and latent diffusion.

DeepFilterNet3 AudioSR (LBM) ERB-Domain Processing
Denoising LogicComplex Multi-Frame Filtering
SR Target48kHz Sample Rate
Latency Factor0.19 CPU RTF
AlgorithmLatent Bridge Diffusion
Stage III: Programmatic Signal Flow
🎛️ Headless DSP & VST3 Orchestration

Bypasses GIL for multi-threaded, studio-grade processing. Analog-modeled EQ, compression, and reverb instantiation via code.

Spotify Pedalboard (JUCE) VST3 Integration RoughRider 3 DSP
Speed Factor300x vs Native Python
Dynamics1-3ms Lookahead Attack
Carving Range200Hz - 500Hz Subtractive
Vocal Naturalizer4.5Hz Humanized Vibrato
🤖 Agentic feedback Loop: The AI Conductor

Utilizes smolagents and Hugging Face Llama-3 to execute a continuous ReAct loop. The conductor "listens" to the final mix, analyzes spectral flatness and centroid metrics, and autonomously refines Pedalboard parameters until "Muddy" or "Harsh" pathologies are eliminated.

ReAct Loop Spectral Flatness Extraction Dynamic Parameter Injection