Audio Signal Processing
The Cognitive Shift in
Audio Engineering
From static, rule-based DSP to dynamic, Reinforcement Learning Agents. The era of the "Black Box" is ending; the era of the "Grey Box" neuro-symbolic system has begun.
Legacy Paradigm (2010-2022)
- Linear Adaptive Filters (FxLMS)
- "Black Box" End-to-End Deep Learning
- MSE Loss (Perceptual Mismatch)
Cognitive State (2023-2025)
- Deep Reinforcement Learning (PPO Agents)
- Differentiable DSP (DDSP) "Grey Box"
- RLHF (Alignment with Human Aesthetics)
The Rise of the Meta-Controller
In Active Noise Control (ANC), traditional algorithms like FxLMS fail with non-linear distortion and impulsive noise. The SOTA solution is DRL-ANC: an RL agent that doesn't generate audio, but *tunes the filters* in real-time.
Algorithm: PPO (Proximal Policy Optimization)
Chosen for stability. Prevents "howling" by clipping policy updates, ensuring smooth filter transitions.
Reward Function Engineering
$R = \alpha·\Delta SNR - \beta·MSE - \gamma·TV(w)$
Includes Total Variation (TV) penalties to prevent "zipper noise" artifacts.
Convergence & Stability Benchmark
Impulsive Noise ScenarioFigure: The Agent observes the error microphone state and adjusts IIR filter coefficients.
Architectural Renaissance
Moving beyond "Black Boxes" to interpretable, differentiable systems.
DDSP
Differentiable DSP
Neural networks predict parameters for oscillators and filters rather than raw samples.
Neural Fields
Spatial Audio
Replacing discrete HRTF tables with continuous neural functions $f(x,y,z) \rightarrow \text{Filter}.$
Diffusion (DiT)
Generative Synthesis
Diffusion Transformers (like EzAudio-DiT) operating in latent space to hallucinate missing frequencies.
The Agent as Engineer
AI is no longer just generating MIDI; it is mixing tracks. Systems like DeepFADE use hierarchical DRL to automate DJ transitions, optimizing for beat alignment and tonal consonance.
SynthRL (Inverse Synthesis)
An RL agent hears a sound and turns the knobs on a VST synthesizer to recreate it. Because RL doesn't need gradients, it works on "Black Box" plugins.
RLHF in Music
MusicRL aligns generative output with human preference. Users rank clips, training a Reward Model that guides the diffusion process toward "aesthetic" quality.