arxiv: 2605.00494 · v1 · submitted 2026-05-01 · 📡 eess.AS

Recognition: unknown

Transformer-based End-to-End Control Filter Generation for Active Noise Control

Boxiang Wang, Qirui Huang, Woon-Seng Gan, Yisong Zou, Zhengding Luo, Ziyi Yang

Authors on Pith no claims yet

Pith reviewed 2026-05-09 19:00 UTC · model grok-4.3

classification 📡 eess.AS

keywords active noise controltransformerend-to-end control filter generationunsupervised learninggenerative fixed-filter ANCdifferentiable systemerror signal training

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The pith

A Transformer directly generates active noise control filters end-to-end in an unsupervised system.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes that a Transformer can produce complete control filters for active noise cancellation straight from input noise signals. It does so by folding the co-processor and real-time controller into one fully differentiable pipeline trained only on the accumulated error signal. This replaces the decomposition and recombination steps of earlier generative fixed-filter methods and removes the need for labeled training data. A reader would care because the change simplifies deployment and reduces error buildup while improving adaptation across different recorded noises.

Core claim

The E2E-CFG framework integrates the co-processor and real-time controller into a single differentiable ANC system so that a Transformer directly outputs control filters. Training uses only the accumulated error signal as the objective function, eliminating the decomposition-reconstruction process required by prior GFANC approaches and thereby avoiding error accumulation from that step. The attention mechanism captures global and dynamic noise features, and numerical tests on real-recorded noises show stronger reduction performance and greater adaptability than the original GFANC framework.

What carries the argument

The fully differentiable ANC pipeline that lets the Transformer generate entire control filters directly, trained unsupervised on accumulated error alone.

If this is right

The control pipeline becomes simpler because decomposition and recombination steps are removed.
Training requires no labeled data and uses only the error signal as objective.
Global noise characteristics are captured through attention without hand-crafted sub-filter combinations.
Performance gains appear on varied real-recorded noises in simulation.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The approach could support online adaptation in embedded devices where only error feedback is available.
Similar end-to-end differentiable designs might apply to other adaptive filtering tasks that currently rely on decomposition.
Real-time hardware implementation would need to confirm that the Transformer inference fits within latency budgets of current ANC systems.

Load-bearing premise

That the integrated differentiable system will produce effective filters when trained solely on the accumulated error signal without separate decomposition.

What would settle it

Numerical tests on the same real-recorded noises showing equal or worse noise reduction than the original GFANC method, or failure of the generated filters to reduce error in a physical ANC setup.

Figures

Figures reproduced from arXiv: 2605.00494 by Boxiang Wang, Qirui Huang, Woon-Seng Gan, Yisong Zou, Zhengding Luo, Ziyi Yang.

**Figure 1.** Figure 1: Overview of the proposed Transformer-based End-to-End Control-Filter Generation view at source ↗

**Figure 2.** Figure 2: Comparison of the proposed E2E-CFG with GFANC and FxNLMS. The two panels view at source ↗

**Figure 3.** Figure 3: Time-varying NMSE curves under sequential noise-type changes. The test signal contains view at source ↗

read the original abstract

To address the limitations of existing Generative Fixed-Filter Active Noise Control (GFANC) methods, which rely on filter decomposition and recombination and require supervised learning with labeled data, this paper proposes a Transformer-based End-to-End Control-Filter Generation (E2E-CFG) framework. Unlike previous approaches that predict combination weights of sub control filters, the proposed method directly generates control filters in an unsupervised manner by integrating the co-processor and real-time controller into a fully differentiable ANC system, where the accumulated error signal is used as the training objective. By abandoning the decomposition--reconstruction process, the proposed design simplifies the control pipeline and avoids error accumulation, while the Transformer architecture effectively captures global and dynamic noise characteristics through its attention mechanism. Numerical simulations on real-recorded noises demonstrate that the proposed method achieves improved noise reduction performance and adaptability to different types of noises compared with the original GFANC framework.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper offers a simpler end-to-end Transformer approach to generating ANC control filters but leaves the performance improvements unquantified.

read the letter

This paper replaces the decomposition and recombination steps in Generative Fixed-Filter Active Noise Control with a Transformer that directly outputs the complete control filter. It trains the model unsupervised by folding the co-processor and controller into one differentiable system and using the accumulated error as the sole objective. The approach is straightforward and removes a source of potential error accumulation from the old pipeline. The attention mechanism is positioned to pick up on longer-term noise patterns across different recorded sounds, which could improve adaptability without needing labeled filter data. On the positive side, the idea of making the entire ANC loop differentiable for direct optimization is sensible if the gradients stay informative. The simulations on real noises are the right kind of test for practical relevance. The main weakness is the lack of concrete results in the abstract. Claims of better noise reduction and adaptability are stated but not quantified, so it's impossible to tell how large the gains are or whether they hold across conditions. The stress-test concern about gradients through the acoustic path convolutions is a real one to examine in the methods section. If the secondary path acts as a filter on the backpropagated signals, the training could become ineffective or unstable for varied noises. I would want to see the exact loss formulation, any stabilization tricks, and side-by-side metrics with error bars before accepting the superiority claim. This work is for the active noise control community, particularly those already familiar with GFANC and looking for ways to integrate modern sequence models. A practitioner building real-time audio systems might find the architecture useful to try, even if the current evidence is preliminary. It is worth sending to peer review. The central idea is coherent and addresses a clear limitation in prior work, though the paper will need stronger empirical support and discussion of training dynamics to be convincing.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes a Transformer-based End-to-End Control-Filter Generation (E2E-CFG) framework for active noise control (ANC). Unlike prior Generative Fixed-Filter ANC (GFANC) methods that rely on supervised filter decomposition and recombination, E2E-CFG integrates the co-processor and real-time controller into a fully differentiable ANC system and trains the Transformer directly in an unsupervised manner using only the accumulated error signal as the objective. The approach claims to simplify the pipeline, avoid error accumulation from decomposition, capture global/dynamic noise features via attention, and achieve improved noise reduction and adaptability on real-recorded noises.

Significance. If the central claims hold, the work would offer a meaningful simplification of ANC control pipelines by removing the decomposition step and enabling unsupervised end-to-end training; the use of a fully differentiable system and Transformer attention for non-stationary noises could improve adaptability in practical settings. The absence of quantitative metrics in the abstract, however, leaves the magnitude of improvement and the robustness of the gradient flow unverified.

major comments (2)

[Training objective and differentiability description (methods)] The central claim that unsupervised training on accumulated error alone suffices for effective filter generation rests on the assumption that gradients remain informative after back-propagation through the secondary-path convolution (and any primary-path model). No analysis of gradient magnitude, vanishing/exploding behavior, or stability under realistic acoustic-path transfer functions is provided; if the paths act as low-pass or attenuating filters, the claimed avoidance of decomposition error and improved adaptability would not necessarily follow.
[Numerical simulations / results] The abstract asserts 'improved noise reduction performance and adaptability' relative to GFANC, yet supplies no quantitative metrics, error bars, statistical tests, or specific noise-type breakdowns. The results section must include explicit dB reductions, convergence curves, and cross-noise comparisons with the original GFANC baseline to substantiate the performance claim.

minor comments (2)

[Methods] Notation for the accumulated error signal and the precise definition of the secondary-path convolution operator should be introduced with an equation early in the methods to avoid ambiguity when discussing differentiability.
[Experimental setup] Dataset details (sampling rates, recording conditions, number of real-recorded noise types) are mentioned only generically; adding a table or explicit list would improve reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed comments. We address each major comment point by point below and outline the revisions we will make to strengthen the manuscript.

read point-by-point responses

Referee: [Training objective and differentiability description (methods)] The central claim that unsupervised training on accumulated error alone suffices for effective filter generation rests on the assumption that gradients remain informative after back-propagation through the secondary-path convolution (and any primary-path model). No analysis of gradient magnitude, vanishing/exploding behavior, or stability under realistic acoustic-path transfer functions is provided; if the paths act as low-pass or attenuating filters, the claimed avoidance of decomposition error and improved adaptability would not necessarily follow.

Authors: We agree that providing an analysis of the gradient flow is valuable to support the claims regarding the differentiability and effectiveness of the unsupervised training. In the revised manuscript, we will include a new subsection in the Methods section dedicated to discussing the back-propagation through the secondary-path convolution. This will cover considerations of gradient magnitude, potential issues with vanishing or exploding gradients, and stability under the acoustic transfer functions employed in our simulations with real-recorded noises. Such analysis will strengthen the justification for the end-to-end approach and its advantages over decomposition-based methods. revision: yes
Referee: [Numerical simulations / results] The abstract asserts 'improved noise reduction performance and adaptability' relative to GFANC, yet supplies no quantitative metrics, error bars, statistical tests, or specific noise-type breakdowns. The results section must include explicit dB reductions, convergence curves, and cross-noise comparisons with the original GFANC baseline to substantiate the performance claim.

Authors: We acknowledge that the abstract and results section would benefit from more explicit quantitative evidence. We will revise the abstract to include specific performance metrics, such as the average noise reduction in dB for different noise types. Additionally, the results section will be expanded to include tables with dB reductions, standard deviations or error bars from repeated experiments, p-values from statistical tests comparing to GFANC, convergence plots over training epochs or time, and detailed cross-comparisons across various real-recorded noise types. These revisions will provide a clearer substantiation of the claimed improvements in noise reduction and adaptability. revision: yes

Circularity Check

0 steps flagged

No circularity: end-to-end differentiable training is independent of prior decomposition

full rationale

The paper's central derivation replaces GFANC's filter decomposition/recombination with direct Transformer generation inside a fully differentiable ANC loop whose loss is the accumulated error signal. No equation is shown to be equivalent to a fitted parameter or prior output by construction. The unsupervised objective and attention-based capture of noise dynamics are presented as new architectural choices, not renamings or self-referential fits. Performance claims rest on external numerical simulations against real-recorded noises, not on internal re-derivation of the same quantities used for training. No load-bearing self-citation or uniqueness theorem is invoked to force the result.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim rests on the domain assumption that a fully differentiable ANC pipeline can be trained unsupervised via error accumulation and that Transformer attention suffices to capture global noise dynamics.

axioms (2)

domain assumption The accumulated error signal serves as a sufficient unsupervised training objective for the end-to-end system.
Invoked when describing the training objective in the proposed framework.
domain assumption Transformer attention mechanism effectively captures global and dynamic noise characteristics.
Stated as the reason for choosing Transformer architecture.

pith-pipeline@v0.9.0 · 5464 in / 1164 out tokens · 34299 ms · 2026-05-09T19:00:39.466063+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

26 extracted references · 1 canonical work pages · 1 internal anchor

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