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REVIEW 3 major objections 6 minor 1 cited by

Attention-Enhanced Short-Time Wiener Solution for Acoustic Echo Cancellation

T0 review · 3 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read An attention-enhanced short-time Wiener solution outperforms established acoustic echo cancellation models on synthetic and blind test sets.

desk verdict A plausible incremental AEC model whose central attention-Wiener block is currently underspecified to the point of non-reproducibility; the empirical gains warrant refereed scrutiny if the authors fill in the missing math. read the letter →

arxiv 2412.18851 v1 pith:FY7JJFXU submitted 2024-12-25 cs.SD

Fei Zhao , Xueliang Zhang This is my paper · ORCID
classification cs.SD
keywords acousticechocancellationshort-timeWienersolutionattentionmechanismdouble-talkICCRNspeechenhancementtime-frequencydomaindeeplearning
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper claims that a deep acoustic echo cancellation model can be improved by feeding it a classical Wiener filter estimate that has been cleaned by an attention mechanism. The proposed method, ASTWS, starts from the ICCRN network and adds an attention-refined short-time Wiener solution, whose echo-reduced output is passed to the network together with the far-end and microphone spectrograms. On a synthetic test set the paper reports higher perceptual speech quality (PESQ) and signal-to-distortion ratio (SDR) than the ICRN, MTFAA, and ICCRN baselines, especially in double-talk, and on the 2023 AEC challenge blind test set it reports the highest MOS ECHO score. The intended implication is that classical filter-theoretic knowledge and attention-based double-talk gating can be combined with a modern neural network at low computational cost.

What carries the argument

The central object is the attention-enhanced short-time Wiener solution: an attention module produces a matrix $A$ from gated versions of the far-end STFT signal, the microphone STFT signal, and a value matrix built from the correlations $X^TX$ and $X^TY$; the paper treats $A$ as supplying enhanced correlation matrices $X^TX_1$ and $X^TY_1$, solves $X^TX_1 H_W = X^TY_1$ for a filter $H_W$, and forms an echo-reduced signal $S_W[t,f] = D[t,f] - \sum_{k=0}^{m-1} H_W[k,f]X[t-k,f]$. This $S_W$ is concatenated with the far-end and microphone STFT inputs to the ICCRN-based AEC module. The Wiener component is the mechanism that injects classical filter-theoretic structure; the attention component is what keeps that structure from being corrupted by double-talk.

What would settle it

Check whether the matrix A produced by the attention module can be split into the claimed enhanced auto-correlation and cross-correlation matrices and whether solving the resulting linear system reproduces the reported echo-reduced signal; if the split is arbitrary or the solve is inconsistent on held-out STFT frames, the Wiener explanation is not load-bearing.

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Extended reading notes

Core claim

The central claim, stated on the paper's own terms, is that the attention-enhanced short-time Wiener solution (ASTWS) outperforms established baseline models on both a synthetic test set and the 2023 AEC challenge blind test set. The paper derives a short-time Wiener solution in the time-frequency domain, uses an attention mechanism to reduce the influence of double-talk on the auto- and cross-correlation terms, and feeds the resulting echo estimate into the ICCRN backbone. The reported results show consistent gains in PESQ and SDR across signal-to-echo ratios, with the largest improvement in double-talk SDR, and a higher MOS ECHO on the blind test set than the larger MTFAA model. The authors conclude that the approach proves both effectiveness and generalization for acoustic echo cancellation.

Load-bearing premise

The load-bearing assumption is that the attention output can be separated into an enhanced auto-correlation matrix and an enhanced cross-correlation matrix, and that solving the resulting linear system yields a filter whose echo-reduced signal genuinely helps the neural network.

Editorial extensions

If this is right

  • If the reported results hold, ASTWS becomes a stronger and cheaper alternative to ICRN and MTFAA, improving double-talk SDR by several decibels at a fraction of the parameter count.
  • The success of the short-time Wiener input suggests that classical filter-theoretic priors can be injected into neural AEC models as input features rather than as replacement filters, which is a general recipe for combining old and new methods.
  • The large SDR jump from the no-attention ablation (STWS) to the full method (ASTWS) indicates that the attention module, not just the Wiener input, is a direct contributor to double-talk performance.
  • Because the Wiener solve is causal and fixed-length with $m=20$ blocks, the input strategy is compatible with streaming, low-latency deployment.
  • The improvement on the 2023 blind test set indicates the gain is not tied to the specific synthetic room settings used in training.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The paper leaves implicit how the value matrix $V$ is formed from $X^TX$ and $X^TY$ and how the attention output $A$ is split back into $X^TX_1$ and $X^TY_1$; making this split explicit and differentiable would let other architectures reuse the same input recipe.
  • A natural test of the mechanism is to freeze the Wiener branch and replace the attention weights with a random fixed gate; if performance is unchanged, the gain is coming from the backbone rather than from the attention-enhanced Wiener estimate.
  • The same attention-gated filter-theoretic input strategy may transfer to other classical estimators such as recursive least squares or Kalman filtering, where double-talk robustness is also the main failure mode.
  • Exposing the filter coefficients $H_W$ themselves to the network, rather than only the echo-reduced signal $S_W$, might let the network correct filter misalignments and improve double-talk performance further.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. This paper presents ASTWS, an acoustic echo cancellation system that combines an attention module with a short-time Wiener solution and an ICCRN-based regression network. The attention module is intended to emphasize single-talk frames and suppress double-talk interference before computing correlation-based filter coefficients; the resulting near-end estimate is fed alongside the far-end and microphone spectrograms into an ICCRN network. Experiments on a self-generated synthetic dataset and the ICASSP 2023 AEC Challenge blind test set report consistent gains over ICRN, MTFAA, and ICCRN, with an ablation (STWS vs. ASTWS) isolating the attention contribution. A code repository link is provided.

Significance. The paper addresses a relevant problem and, if the method is fully specified, the empirical results are potentially useful: ASTWS reports substantially higher double-talk SDR (e.g., 23.46 dB at SER=0 vs. 15.37 dB for MTFAA) with lower complexity than MTFAA, and also improves over its ICCRN backbone with modest overhead. The inclusion of a blind test set, an ablation study, and a code link are strengths. However, the central contribution—the attention-enhanced short-time Wiener solution—is described only at the level of a diagram and a few equations; the key construction V and the splitting of the attention output into correlation matrices are not defined. Until these are specified, the reported gains cannot be attributed to the advertised Wiener solution, and independent replication is impossible. The work is therefore not yet in a publishable form.

major comments (3)
  1. [Section III-B, Eqs. (9)-(13)] V is never defined. The text states only that V is 'a combination of X^T X and X^T Y, as articulated in Equation 8,' but no explicit formula is given, and the shapes of V, Q, and K after the gating transformations are not specified. Since V1 and hence A depend on this undefined quantity, Equations (9)-(13) do not define a reproducible attention output. Please give the exact construction of V (including dimensions and any normalization) and state the shapes of all intermediate tensors.
  2. [Section III-B to III-C, Eqs. (13)-(14)] The paper asserts that the attention output A is an enhanced representation of X^T X and X^T Y and 'denotes' these enhanced versions as X^T X1 and X^T Y1, but no rule is given for decomposing A into these two matrices. Without such a rule, solving (X^T X1) H_W = X^T Y1 is not a well-defined function of X and Y. This is the load-bearing step of the proposed method: it is the only connection between the attention mechanism and the Wiener solution, and it determines whether the label 'Wiener solution' is justified. Please provide the decomposition algorithm explicitly, or redefine the computation so that H_W follows from well-defined correlation matrices.
  3. [Section III-C and Section III-D] The invertibility and numerical solution of the normal equations are not addressed. The matrix X^T X1 (or its regularized version) is not guaranteed to be invertible, yet no pseudo-inverse, diagonal loading, or fallback is described. In addition, H_W is not directly supervised—the loss in Equations (15)-(18) is applied only to the final network output—so it is unclear whether the reported improvements come from solving the Wiener equations or simply from feeding an extra learned feature to ICCRN. Please specify the linear solver and regularization, and consider an ablation that replaces H_W with a randomly initialized or unregularized correlation feature to demonstrate the specific contribution of the Wiener solution.
minor comments (6)
  1. [Table I] Table I contains an unexplained row beginning 'mix 1.33 -10 1.93 0 2.56 10 ...' that appears to be a formatting artifact of the header; it should be removed or labeled.
  2. [Section V] Section V states that ASTWS achieves 'a greater performance improvement at a reduced computational expense relative to the ICCRN,' but Table I lists ASTWS at 0.963 GMacs / 0.148M parameters versus ICCRN at 0.844 GMacs / 0.12M parameters; the sentence should be corrected.
  3. [Section IV-B] MTFAA is modified by removing its band decomposition and band merging modules; please clarify whether this is the standard configuration and discuss the effect of this modification on the reported baseline performance.
  4. [Equations (2) and (9)-(13)] The notation K is used both for the number of filter blocks in Eq. (2) and for the attention Key input in Eqs. (9)-(13); rename one of them to avoid ambiguity.
  5. [Equation (15)] Equation (15) defines the S-SISNR loss through beta and cos(beta) but does not state how beta is computed from the predicted and target signals; a reference or explicit formula is needed.
  6. [Throughout] The paper alternates between 'short-time Wiener solution' and 'short-term Wiener solution'; please use a single term consistently.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: the Wiener solution is classically derived, the empirical claims are benchmarked against external baselines, and the only self-citation (ICCRN) is used as a backbone/baseline rather than as the justification for the reported gains.

full rationale

The core derivation chain is not circular. In Section II-B, the Wiener solution is obtained from a standard MSE cost (Eq. 5), whose derivative gives the Wiener-Hopf equations (Eqs. 6-7), restated in the T-F domain as Eq. 8. This is a classical first-principles derivation, not a definition of the target output in terms of itself. The attention-enhanced variant (Eqs. 9-13) introduces learnable gating vectors q, k, v and produces an intermediate attention output A, which is then used to form the short-time Wiener filter H_W in Section III-C. H_W is not supervised directly and is not a fitted parameter renamed as a prediction; it is an input feature to the ICCRN-based network, and the final near-end estimate is trained with a multi-term loss. The empirical evaluation compares ASTWS with ICRN, MTFAA, and ICCRN on a synthetic test set and with blind test results from ICASSP 2023, so the central claims are anchored to external benchmarks rather than to the paper's own fitted values. The main self-citation, ICCRN [16], is co-authored by the second author and serves as the architectural backbone and as a baseline, but the paper's improvement claim rests on the reported experiments, not on an argument that reduces to that citation. The paper's genuine weakness is underspecification: V is only described as 'a combination of X^T X and X^T Y' with no formula, and there is no rule for decomposing the single attention output A into X^T X1 and X^T Y1; this is a reproducibility and rigor problem, not a circularity problem. No equation in the paper reduces to its own input by construction, and no fitted quantity is relabeled as a prediction. Score 2 reflects only the minor, non-load-bearing self-citation and the incomplete definition of the Wiener input, not circular reasoning.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The central method rests on standard Wiener theory plus a set of hand-chosen hyperparameters and an underspecified attention-gating construction. The attention and network weights are trained, but the architectural choices m, p, and the loss weighting are fixed without ablation. No new physical or mathematical entities are postulated.

free parameters (3)
  • Filter block count m = 20
    Chosen by hand in Section IV-B ('For m in subsection III-C, we set it as 20'); controls the FIR filter length in the short-time Wiener solution and therefore the echo path modeling granularity.
  • Spectral compression factor p = 0.5
    Set for the RI+Mag loss in Eqs (16)-(17); no ablation or motivation is given for this value.
  • Loss weighting = equal weights (1 for LRI, Lmag, Ls-sisnr)
    The total loss in Eq (18) is a simple sum; no per-term weighting is motivated or ablated.
assumptions (5)
  • standard math An optimal linear FIR echo canceller minimizes MSE and solves h = R^{-1} r (Eq 7).
    Invoked in Section II-B without proof; this is the classical Wiener solution.
  • domain assumption The echo path can be represented in the STFT domain as a per-frequency FIR over K frames (Eq 2), ignoring cross-band leakage.
    Section II-A Eq (2); a standard but approximate model for room impulse responses in the time-frequency domain.
  • domain assumption The Wiener derivation assumes far-end single talk, d(n)=y(n), with no near-end speech.
    Section II-B states this condition; the method then relies on attention to handle double-talk, but the validity of the gated correlation trick is not proven.
  • ad hoc to paper Solving (X^T X1) H_W = X^T Y1, with X^T X1 and X^T Y1 split from the attention output A, produces a filter H_W useful for echo cancellation.
    Section III-B to III-C asserts this without derivation. No optimization problem is specified for the attention-gated statistics, and the split of A is undefined.
  • domain assumption m=20 filter blocks sufficiently capture the echo path in the time-frequency domain.
    Section IV-B sets m=20 with no analysis of the echo path length or the approximation error.

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Cite this review

Pith. "Pith review of Attention-Enhanced Short-Time Wiener Solution for Acoustic Echo Cancellation." pith.science (2026). https://pith.science/paper/FY7JJFXU

@misc{pith2026241218851,
  author       = {Pith},
  title        = {Pith review of: Attention-Enhanced Short-Time Wiener Solution for Acoustic Echo Cancellation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FY7JJFXU}},
  note         = {Machine review of arXiv:2412.18851}
}
read the original abstract

Acoustic Echo Cancellation (AEC) is an essential speech signal processing technology that removes echoes from microphone inputs to facilitate natural-sounding full-duplex communication. Currently, deep learning-based AEC methods primarily focus on refining model architectures, frequently neglecting the incorporation of knowledge from traditional filter theory. This paper presents an innovative approach to AEC by introducing an attention-enhanced short-time Wiener solution. Our method strategically harnesses attention mechanisms to mitigate the impact of double-talk interference, thereby optimizing the efficiency of knowledge utilization. The derivation of the short-term Wiener solution, which adapts classical Wiener solutions to finite input causality, integrates established insights from filter theory into this method. The experimental outcomes corroborate the effectiveness of our proposed approach, surpassing other baseline models in performance and generalization. The official code is available at https://github.com/ZhaoF-i/ASTWS-AEC

Figures

Figures reproduced from arXiv: 2412.18851 by the authors.

Figure 1
Figure 1. The schematic flowchart of the overall structure. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Diagram of the attention-enhanced Wiener solution [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Room Impulse Response as a Prompt for Acoustic Echo Cancellation

    cs.SD 2025-05 conditional novelty 6.0 of 10

    Feeding a measured room impulse response into a neural acoustic echo canceller, especially as a synthesized echo input, improves its performance on unseen and real rooms.

Reference graph

Works this paper leans on

23 extracted references · 20 canonical work pages · cited by 1 Pith paper

  1. [1]

    An adaptive echo canceller,

    MM Sondhi, “An adaptive echo canceller,” Bell System technical journal, vol. 46, no. 3, pp. 497–511, 1967

    work page 1967

  2. [2]

    Advances in network and acoustic echo cancel- lation,

    Jacob Benesty, Tomas G ¨ansler, Dennis R Morgan, M Mohan Sondhi, Steven L Gay, et al., “Advances in network and acoustic echo cancel- lation,” 2001

    work page 2001

  3. [3]

    Acoustic echo control,

    Gerald Enzner, Herbert Buchner, Alexis Favrot, and Fabian Kuech, “Acoustic echo control,” in Academic press library in signal processing, vol. 4, pp. 807–877. Elsevier, 2014

    work page 2014

  4. [4]

    Eberhard H ¨ansler and Gerhard Schmidt, Acoustic echo and noise control: a practical approach , John Wiley & Sons, 2005

    work page 2005

  5. [5]

    An overview on optimized NLMS algorithms for acoustic echo cancellation,

    Constantin Paleologu, Silviu Ciochina, Jacob Benesty, and Steven L. Grant, “An overview on optimized NLMS algorithms for acoustic echo cancellation,” EURASIP J. Adv. Signal Process., vol. 2015, pp. 97, 2015

    work page 2015

  6. [6]

    An overview on optimized nlms algorithms for acoustic echo cancellation,

    Constantin Paleologu, Silviu Ciochin ˘a, Jacob Benesty, and Steven L Grant, “An overview on optimized nlms algorithms for acoustic echo cancellation,” EURASIP Journal on Advances in Signal Processing , vol. 2015, pp. 1–19, 2015

    work page 2015

  7. [7]

    Simon S Haykin, Adaptive filter theory, Pearson Education India, 2005

    work page 2005

  8. [8]

    A complex spectral mapping with inplace convolution recurrent neural networks for acoustic echo cancellation,

    Chenggang Zhang, Jinjiang Liu, and Xueliang Zhang, “A complex spectral mapping with inplace convolution recurrent neural networks for acoustic echo cancellation,” in IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2022, Virtual and Singapore, 23-27 May 2022 . 2022, pp. 751–755, IEEE

    work page 2022

Show all 23 references
  1. [9]

    Neural multi-channel and multi-microphone acoustic echo cancellation,

    Chenggang Zhang, Jinjiang Liu, Hao Li, and Xueliang Zhang, “Neural multi-channel and multi-microphone acoustic echo cancellation,” IEEE ACM Trans. Audio Speech Lang. Process., vol. 31, pp. 2181–2192, 2023

    2023

  2. [10]

    Multi-scale temporal frequency convolutional network with axial atten- tion for speech enhancement,

    Guochang Zhang, Libiao Yu, Chunliang Wang, and Jianqiang Wei, “Multi-scale temporal frequency convolutional network with axial atten- tion for speech enhancement,” in ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 202...

    2022

  3. [11]

    ICASSP 2023 acoustic echo cancellation challenge,

    Ross Cutler, Ando Saabas, Tanel P ¨arnamaa, Marju Purin, Evgenii Inden- bom, Nicolae-Catalin Ristea, Jegor Guzvin, Hannes Gamper, Sebastian Braun, and Robert Aichner, “ICASSP 2023 acoustic echo cancellation challenge,” CoRR, vol. abs/2309.12553, 2023

    2023 arXiv

  4. [12]

    ICASSP 2022 acoustic echo cancellation challenge,

    Ross Cutler, Ando Saabas, Tanel P ¨arnamaa, Marju Purin, Hannes Gam- per, Sebastian Braun, Karsten Sørensen, and Robert Aichner, “ICASSP 2022 acoustic echo cancellation challenge,” in IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2022, Virtua...

    2022

  5. [13]

    Kalmannet: Data-driven kalman filtering,

    Guy Revach, Nir Shlezinger, Ruud JG Van Sloun, and Yonina C Eldar, “Kalmannet: Data-driven kalman filtering,” in ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021, pp. 3905–3909

    2021

  6. [14]

    Low- complexity acoustic echo cancellation with neural kalman filtering,

    Dong Yang, Fei Jiang, Wei Wu, Xuefei Fang, and Muyong Cao, “Low- complexity acoustic echo cancellation with neural kalman filtering,” in ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2023, pp. 1–5

    2023

  7. [15]

    Kalmannet: A learnable kalman filter for acoustic echo cancellation,

    Yixuan Zhang, Meng Yu, Hao Zhang, Dong Yu, and DeLiang Wang, “Kalmannet: A learnable kalman filter for acoustic echo cancellation,” CoRR, vol. abs/2301.12363, 2023

    2023 arXiv

  8. [16]

    Iccrn: Inplace cepstral convolutional recurrent neural network for monaural speech enhancement,

    Jinjiang Liu and Xueliang Zhang, “Iccrn: Inplace cepstral convolutional recurrent neural network for monaural speech enhancement,” in ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2023, pp. 1–5

    2023

  9. [17]

    Room impulse response generator,

    Emanuel AP Habets, “Room impulse response generator,” Technische Universiteit Eindhoven, Tech. Rep , vol. 2, no. 2.4, pp. 1, 2006

    2006

  10. [18]

    Funnel deep complex u-net for phase-aware speech enhancement,

    Yuhang Sun, Linju Yang, Huifeng Zhu, and Jie Hao, “Funnel deep complex u-net for phase-aware speech enhancement,” in Interspeech 2021, 22nd Annual Conference of the International Speech Commu- nication Association, Brno, Czechia, 30 August - 3 September 2021 , Hynek Hermansky,...

    2021

  11. [19]

    Conv-tasnet: Surpassing ideal time- frequency magnitude masking for speech separation,

    Yi Luo and Nima Mesgarani, “Conv-tasnet: Surpassing ideal time- frequency magnitude masking for speech separation,” IEEE ACM Trans. Audio Speech Lang. Process. , vol. 27, no. 8, pp. 1256–1266, 2019

    2019

  12. [20]

    Image method for efficiently simulating small-room acoustics,

    Jont B Allen and David A Berkley, “Image method for efficiently simulating small-room acoustics,” The Journal of the Acoustical Society of America, vol. 65, no. 4, pp. 943–950, 1979

    1979

  13. [21]

    Perceptual evaluation of speech quality (pesq)-a new method for speech quality assessment of telephone networks and codecs,

    Antony W. Rix, John G. Beerends, Michael P. Hollier, and Andries P. Hekstra, “Perceptual evaluation of speech quality (pesq)-a new method for speech quality assessment of telephone networks and codecs,” in IEEE International Conference on Acoustics, Speech, and Signal Processi...

    2001

  14. [22]

    Performance measurement in blind audio source separation,

    Emmanuel Vincent, R ´emi Gribonval, and C ´edric F´evotte, “Performance measurement in blind audio source separation,” IEEE transactions on audio, speech, and language processing , vol. 14, no. 4, pp. 1462–1469, 2006

    2006

  15. [23]

    Adam: A method for stochastic optimization,

    Diederik P. Kingma and Jimmy Ba, “Adam: A method for stochastic optimization,” in 3rd International Conference on Learning Represen- tations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, Yoshua Bengio and Yann LeCun, Eds., 2015

    2015

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