NoiseController Framework

• NoiseController is a family of frameworks that systematizes the control and decomposition of various noise types, enhancing stability and fidelity in complex environments.
• It employs algorithmic innovations like compensation filters and weight-constrained FXLMS to mitigate crosstalk, delay-induced instability, and communication constraints.
• The framework is implemented on embedded systems and extended to deep-learning paradigms, supporting applications from distributed acoustic ANC to generative video modeling.

NoiseController refers to a family of advanced frameworks for structured control or decomposition of "noise"—interpreted variably as stochastic disturbance in signal processing, physical environmental noise in active noise control (ANC), or even Gaussian noise in generative diffusion models—so as to achieve system-level objectives that are nontrivial under standard paradigms. Across disciplines such as distributed signal processing, real-time embedded ANC, robustness without internode communication, proactive distributed adaptation, and multi-view generative video modeling, the NoiseController paradigm systematizes robust, scalable interventions on noise, achieving high attenuation, stability, consistency, or spatiotemporal fidelity under realistic and often adverse constraints.

1. Distributed ANC and Communication-Robust NoiseController Architectures

NoiseController as formulated in distributed acoustic ANC focuses on addressing the canonical limitations of traditional multichannel ANC (MCANC): computational burden, sensitivity to communication delays, and failure to mitigate crosstalk (Ji et al., 2023, Ji et al., 16 Jul 2025, Ji et al., 1 Oct 2025). Nodes are spatially distributed, each embedding a reference microphone, a local controller (with filter $\psi_k(n)$), an actuator (loudspeaker), and an error microphone. Key architectural strategies include:

• Sparse peer-to-peer communication graphs: Each node communicates only with its immediate neighbors, limiting bandwidth and lowering synchronization complexity.
• Compensation filters ($c_{km}(n)$): These invert cross-path effects between nodes’ actuators and sensors, rendering the system invariant to cross-talk in the underlying acoustic propagation matrix.
• Independent local filter updates: Each node adapts only its local filter based solely on the local error and filtered reference, using neighbor data only to assemble the global compensating control signal for emission.
• Delay and packet loss resilience: The adaptation law is robust to lossy communication, as neighbor filters are used only in the compensatory term and local adaptation continues even when neighbor updates are stale.

This architecture achieves mean-squared-error and spectral attenuation performance nearly indistinguishable from centralized MCANC, with verified stability for communication rates as low as $0.1\%$ and network delays up to 187 ms. Convergence times of $5 \times 10^4$ to $10^5$ samples (at 16 kHz) are typical (Ji et al., 2023).

2. Algorithmic Innovations: Compensation, Constraints, and Proactivity

Key algorithmic elements distinguish modern NoiseController frameworks from classic feedforward or feedback ANC:

• Compensation Filter Design (Ji et al., 2023): Secondary paths from nonlocal speakers are modeled as convolutions of the self-path and a compensation filter, identified off-line via filtered-x LMS. This enables each node’s control output to selectively cancel cross-node acoustic interference.
• Weight-Constrained Filtered-X LMS (WCFxLMS) (Ji et al., 16 Jul 2025): Internode crosstalk can induce divergence or degraded convergence in standard distributed ANC when operating without communication. WCFxLMS regularizes each local update with a quadratic penalty $\lambda\|w_k(n) - \tilde{w}_k\|^2$, where $\tilde{w}_k$ is a periodically refreshed “center” filter that serves as a moving reference. The “self-boosted” variant autonomously updates $\tilde{w}_k$ using the local residual-noise performance metric to maintain both robustness and adaptivity.
• Proactive Communication and Adaptive-Fixed Switching (Ji et al., 1 Oct 2025): Instability under delayed or sporadic communication is addressed by a hybrid mode where adaptation is suspended and local filters frozen whenever the residual noise increases beyond a preset threshold, simultaneously prompting the exchange of accumulated gradients between neighbors. Upon receiving new data, nodes update control filters by a mixed-gradient strategy incorporating local and neighbor increments via compensation filters, then resume adaptation.

A summary of these strategies is as follows:

Algorithmic Feature	Description	Reference
Compensation Filters	Cross-path decorrelation/inversion per node	(Ji et al., 2023)
WCFxLMS with Self-Boost	Local weight projection with performance-based center	(Ji et al., 16 Jul 2025)
Proactive Comm. and Switch	Mode switching + gradient aggregation under degradation	(Ji et al., 1 Oct 2025)

3. Embedded and Modular Real-Time NoiseController Implementations

NoiseController frameworks have been realized on embedded DSPs and scalable hardware for real-world deployment (Zhaohan, 2023, Gan et al., 2024). Implementation is characterized by:

• Modular signal chains: Separation of pre-processing, secondary-path training (NLMS/LMS), run-time adaptation (FXLMS variants), and post-processing.
• Power-constrained adaptation: Quadratically constrained update rules, such as MOV-FxLMS, enforce instantaneous or running output power bounds for actuator protection and prevention of nonlinearity, with time-varying penalty factors $\gamma(n)$ or $\alpha(n)$ modulating the adaptation (Gan et al., 2024).
• Fully remote control and monitoring: Embedded wireless modules (e.g., ESP32, Raspberry Pi broker) and web interfaces facilitate parameter tuning, real-time status visualization, and distributed deployment orchestration.
• Deployment in open, unstructured environments: Validation on acoustic benches, free-field scenarios, and heavy machinery demonstrates 6–12 dB tonal attenuation, with robustness to source movement, environmental variation, and network-induced variability (Zhaohan, 2023, Gan et al., 2024).

4. Multi-Channel, Deep, and Hybrid Framework Extensions

The NoiseController concept extends to fully multi-channel, deep-learning-infused, or hybrid statistical-adaptive paradigms (Luo et al., 2022, Luo et al., 2022, Yu, 2024):

• Automatic differentiation of multi-channel ANC: By interpreting McFxLMS/McFxNLMS as differentiable computation graphs, NoiseController allows rapid prototyping and integration with contemporary deep learning toolchains (e.g., PyTorch), enabling batch processing, GPU acceleration, and the seamless extension to complex signal and network topologies (Luo et al., 2022).
• Hybrid model-based + data-driven selection: Frameworks integrating 1D-CNN modules for optimal pre-trained filter selection (SFANC) with low-latency, recursive NLMS adaptation in the main loop realize both immediate and asymptotic suppression, and track non-stationary, composite noise sources (Luo et al., 2022).
• Optimal statistical filtering: Kalman filter-based NoiseControllers treat the control filter coefficients as the hidden state in a linear Gaussian dynamical system, offering much faster convergence (by a factor of 5–10 in simulation) than gradient-based adaptation, particularly for dynamic, nonstationary disturbances (Yu, 2024).

5. Theoretical Guarantees, Performance, and Limitations

Recent NoiseController designs are accompanied by empirical and theoretical analyses of stability, robustness, resource constraint compliance, and optimality:

• Communication resilience: Simulations affirm performance parity with centralized MCANC across challenging delay/loss regimes; local-only adaptation prevents instability under arbitrarily low neighbor-data rates (Ji et al., 2023, Ji et al., 1 Oct 2025).
• Resource and power constraints: Embedded platforms enforce explicit actuator constraints, monitor for clipping, and autonomously suspend/resume learning upon detecting limits (Gan et al., 2024).
• Performance metrics: Attenuation is measured via mean-squared error (MSE), average normalized squared error (ANSE), and signal-to-noise ratios, with steady-state reductions of 10–30 dB in broadband and tonal regimes documented.
• Limitations: All practical realizations highlight constraints such as pre-identification of secondary paths, finite adaptation frame length, memory/compute scaling with filter size and node count, and restricted adaptation under rapid transients or unmodeled noise classes (Luo et al., 2022, Luo et al., 2022, Yu, 2024).

6. Generalizations and Non-Acoustic Domains: Integral-Constrained and Generative Control

The NoiseController nomenclature also labels structurally related frameworks for different types of noise-controlled systems:

• Nonlinear control with integral constraints: In mobile agent trajectory planning (e.g., quadrotors emitting noise), the NoiseController applies high-order control barrier functions (CBFs) with trajectory-integral constraints, ensuring that cumulative noise at sensitive locations never exceeds thresholds. Efficient over-approximation strategies enforce limits over lines and polygons, and optimal controllers are synthesized via real-time convex QPs (Seidu et al., 2024).
• Consistent noise control in generative models: In the video generation context, NoiseController decomposes initial Gaussian noise into background/foreground, shared/residual components, and orchestrates inter-frame/inter-view collaboration via structured matrices. Parallel U-Nets jointly denoise these decomposed streams, achieving state-of-the-art spatiotemporal consistency for multi-view diffusion video generation (Dong et al., 25 Apr 2025).

7. Applications and Outlook

NoiseController frameworks are applied in distributed ANC for automotive, industrial, and acoustic metamaterial domains; in portable or scalable noise mitigation for construction and appliances; in robust, communication-agnostic, or hybrid adaptive filtering; in resource-sensitive embedded systems; and in structured generative modeling for perception-critical video. Key research directions include:

• Enhanced autonomy and self-management of filter adaptation based on observed performance or constraints.
• Integration of data-driven modules (DNNs, reinforcement learning) into adaptive, model-based control pipelines.
• Real-time synchronization and scaling to large networks with minimal bandwidth through sparse, event-triggered communication.
• Extension to non-acoustic, nonlinear, and multi-modal control regimes where “noise” represents a generalized, system-level disturbance subject to structured control.

Representative works include (Ji et al., 2023, Zhaohan, 2023, Ji et al., 16 Jul 2025, Ji et al., 1 Oct 2025, Gan et al., 2024, Luo et al., 2022, Luo et al., 2022, Yu, 2024, Seidu et al., 2024), and (Dong et al., 25 Apr 2025).