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arxiv: 2607.02062 · v1 · pith:A32A2KYZnew · submitted 2026-07-02 · 📡 eess.AS

LMPAN: A Lightweight Multi-Path Alignment Network for Joint Full-Duplex Acoustic Echo Cancellation and Noise Suppression

Chengwei Liu , Shaofei Xue , Haoyin Yan , Xiaotao Liang , Zheng Xue This is my paper

Pith reviewed 2026-07-03 05:04 UTC · model grok-4.3

classification 📡 eess.AS
keywords acoustic echo cancellationnoise suppressionlightweight neural networkfull-duplex systemsmulti-path alignmentattention mechanismon-device processingself-supervised learning
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The pith

LMPAN achieves performance comparable to DeepVQE-S for joint full-duplex echo cancellation and noise suppression using only 480K parameters and real-time inference.

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

The paper proposes LMPAN as a lightweight network for on-device joint acoustic echo cancellation and noise suppression in full-duplex spoken dialogue systems. It introduces a multi-path alignment stage to correct temporal and energy mismatches, an attention-based mechanism for dynamic feature integration, and a post-filtering module with dynamic target generation. A two-stage training process incorporates self-supervised learning representations to boost perceptual quality. Experiments demonstrate that the model matches state-of-the-art lightweight performance while meeting real-time requirements on devices.

Core claim

LMPAN performs joint full-duplex acoustic echo cancellation and noise suppression through a multi-path alignment stage that corrects temporal and energy mismatches across reference, linear AEC output, and microphone signals, followed by an attention-based mechanism that dynamically integrates enhanced features under varying conditions and a post-filtering module with dynamic target generation for downstream tasks. The network is trained in two stages leveraging self-supervised learning representations, resulting in a model with 480K parameters and 126 MACs that achieves performance comparable to DeepVQE-S while supporting real-time inference.

What carries the argument

Multi-path alignment stage that corrects temporal and energy mismatches across reference, LAEC output, and microphone signals, paired with an attention-based mechanism for dynamic feature integration.

If this is right

  • The model enables real-time on-device processing for full-duplex spoken dialogue systems without cloud offloading.
  • The post-filtering module with dynamic targets improves compatibility with downstream tasks such as ASR and VAD.
  • Two-stage training with self-supervised representations enhances perceptual quality under varying acoustic scenarios.
  • Low parameter count and MACs support deployment on resource-constrained hardware while maintaining comparable performance.

Where Pith is reading between the lines

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

  • On-device processing could reduce latency and privacy risks by keeping audio handling local rather than sending data to servers.
  • The alignment approach might extend to other multi-signal audio problems such as multi-microphone setups or beamforming.
  • Further hardware-specific testing could reveal whether the design maintains robustness across different microphone and speaker configurations in consumer devices.

Load-bearing premise

The multi-path alignment stage and attention-based mechanism can reliably correct mismatches and integrate features across diverse acoustic conditions and hardware distortions.

What would settle it

A controlled test on recordings with large temporal offsets or energy mismatches between signals where LMPAN metrics fall below DeepVQE-S would show the alignment and attention components do not deliver the claimed correction and integration.

Figures

Figures reproduced from arXiv: 2607.02062 by Chengwei Liu, Haoyin Yan, Shaofei Xue, Xiaotao Liang, Zheng Xue.

Figure 1
Figure 1. Figure 1: Full-Duplex Spoken Dialogue Architecture. ditional signal processing components [12], typically incorpo￾rating a linear AEC (LAEC) module based on adaptive filter algorithms to suppress linear echo components. Additionally, multi-task learning frameworks [8,13,14] have shown effective￾ness in acoustic scenarios by jointly addressing NS, DRB, and AEC, leading to overall improvements in speech quality. To ad… view at source ↗
Figure 2
Figure 2. Figure 2: Overall structure of the proposed LMPAN system. Details for key components are given in: (a) Overall diagram of the proposed framework of LMPAN, (b) Alignment block, (c) Attention fusion module. expressed as y = s + n + e (1) where y, s, n, and e denote the microphone signal, near-end speech, additive noise, and echo component, respectively. The echo component e is generated by convolving the far-end refer… view at source ↗
Figure 3
Figure 3. Figure 3: Two-stage training pipeline for LMPAN: Stage 1: SSL representation alignment using a frozen pretrained WavLM model; Stage 2 jointly optimizes spectral fidelity, echo suppres￾sion, and perceptual quality, with SSL loss as a consistency reg￾ularizer. where SNRin and SNRt denote the input SNR and target SNR, respectively. (b) An echo residual factor β controlled by a de￾sired target signal-to-echo ratio (SER)… view at source ↗
read the original abstract

We propose a lightweight multi-path alignment network (LMPAN) for on-device joint acoustic echo cancellation (AEC) and noise suppression (NS) in full-duplex spoken dialogue systems. To address hardware-induced distortions and dynamic acoustic conditions, we introduce three core innovations: (1) a multi-path alignment stage correcting temporal and energy mismatches across reference, linear AEC (LAEC) output, and microphone signals; (2) an attention-based mechanism that dynamically integrates enhanced LAEC and microphone features under varying acoustic scenarios; (3) a post-filtering module with a dynamic target generation strategy for downstream tasks (ASR, VAD). Furthermore, we adopt a two-stage training framework leveraging self-supervised learning representations to enhance perceptual quality. Experiments show that LMPAN, with only 480K parameters and 126 MACs, achieves performance comparable to the state-of-the-art lightweight model DeepVQE-S, while ensuring real-time inference capability.

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.

Referee Report

2 major / 0 minor

Summary. The manuscript proposes LMPAN, a lightweight multi-path alignment network for joint full-duplex acoustic echo cancellation (AEC) and noise suppression (NS). It introduces three innovations: (1) a multi-path alignment stage to correct temporal and energy mismatches across reference, LAEC output, and microphone signals; (2) an attention-based mechanism for dynamic feature integration under varying conditions; (3) a post-filtering module with dynamic target generation. A two-stage training framework using self-supervised learning representations is adopted. The central claim is that LMPAN (480K parameters, 126 MACs) achieves performance comparable to DeepVQE-S while supporting real-time inference on-device.

Significance. If the performance claims hold with proper validation, the work would provide a practical contribution to efficient on-device audio processing for full-duplex spoken dialogue systems, addressing hardware distortions at low computational cost (480K params / 126 MACs). The emphasis on real-time capability and downstream task compatibility (ASR, VAD) aligns with deployment needs in resource-constrained environments.

major comments (2)
  1. [Abstract] Abstract: The headline claim that LMPAN achieves performance comparable to the state-of-the-art lightweight model DeepVQE-S is stated without any supporting metrics, datasets, baselines, error analysis, or quantitative results. This leaves the central experimental claim without verifiable evidence.
  2. [Abstract] Abstract (and implied experimental section): No ablation studies, removal experiments, or per-component metric deltas are reported for the multi-path alignment stage or the attention-based integration mechanism. These are presented as core innovations responsible for mismatch correction and dynamic feature integration, yet their specific contributions cannot be assessed.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments, which highlight areas where the abstract and experimental reporting can be strengthened. We address each point below and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The headline claim that LMPAN achieves performance comparable to the state-of-the-art lightweight model DeepVQE-S is stated without any supporting metrics, datasets, baselines, error analysis, or quantitative results. This leaves the central experimental claim without verifiable evidence.

    Authors: We agree that the abstract would benefit from explicit quantitative support. In the revised manuscript, we will augment the abstract with key metrics (e.g., ERLE, PESQ, STOI) from the AEC-Challenge and internal datasets, along with direct comparisons to DeepVQE-S and other baselines. The full experimental details, including error analysis, remain in Section 4. revision: yes

  2. Referee: [Abstract] Abstract (and implied experimental section): No ablation studies, removal experiments, or per-component metric deltas are reported for the multi-path alignment stage or the attention-based integration mechanism. These are presented as core innovations responsible for mismatch correction and dynamic feature integration, yet their specific contributions cannot be assessed.

    Authors: We acknowledge the absence of ablation studies for the multi-path alignment and attention-based integration components. We will add a dedicated ablation subsection in the experimental results (Section 4) that reports performance deltas when each module is removed, using the same evaluation metrics and datasets. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical model proposal with external benchmarks

full rationale

The paper proposes an empirical neural network architecture (LMPAN) for joint AEC and NS, describing three architectural innovations and reporting experimental performance against an external baseline (DeepVQE-S). No equations, derivations, or first-principles predictions are presented that reduce by construction to fitted parameters, self-defined quantities, or self-citation chains. All performance claims reference independent comparison models and datasets outside the paper's own fitted values, satisfying the criteria for a self-contained empirical result.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 3 invented entities

The work rests on standard domain assumptions about linear echo paths and additive noise plus learned neural-network parameters; three new modules are introduced without independent falsifiable evidence outside the reported experiments.

free parameters (1)
  • Network weights
    480K parameters learned during the two-stage training process on audio data.
axioms (1)
  • domain assumption Linear acoustic echo paths and additive background noise can be modeled and mitigated by a combination of linear AEC followed by nonlinear neural stages.
    Invoked in the design of the LAEC output path and the subsequent alignment and attention stages.
invented entities (3)
  • Multi-path alignment stage no independent evidence
    purpose: Correct temporal and energy mismatches across reference, LAEC, and microphone signals.
    New module introduced to handle hardware distortions; no external validation cited.
  • Attention-based integration mechanism no independent evidence
    purpose: Dynamically combine enhanced LAEC and microphone features under varying conditions.
    New dynamic weighting component proposed for scenario adaptation.
  • Post-filtering module with dynamic target generation no independent evidence
    purpose: Produce outputs optimized for downstream ASR and VAD tasks.
    New target-generation strategy introduced in the final stage.

pith-pipeline@v0.9.1-grok · 5708 in / 1428 out tokens · 31492 ms · 2026-07-03T05:04:59.974749+00:00 · methodology

discussion (0)

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Reference graph

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    Introduction Full-duplex spoken dialogue systems (FDSDS) have made re- markable progress with the development of large language mod- els (LLMs), enabling more natural interactions [1,2]. However, their performance degrades substantially under adverse echo and noise conditions [3], highlighting the critical importance of acoustic echo cancellation (AEC) an...

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    LMPAN: A Lightweight Multi-Path Alignment Network for Joint Full-Duplex Acoustic Echo Cancellation and Noise Suppression

    METHODOLOGY 2.1. Problem formulation We assume an FDSDS in Fig. 1, where near-end speech is con- taminated by echo and noise. The observed signal model can be arXiv:2607.02062v1 [eess.AS] 2 Jul 2026 Figure 2:Overall structure of the proposed LMPAN system. Details for key components are given in: (a) Overall diagram of the proposed framework of LMPAN, (b) ...

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