Pith. sign in

REVIEW 3 major objections 5 minor 44 references

An Investigation on Combining Geometry and Consistency Constraints into Phase Estimation for Speech Enhancement

T0 review · 3 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read The paper claims that alternating speech-noise projections converge within about five iterations to one of the two valid phase candidates, resolving the sign ambiguity without a trained sign predictor.

desk verdict A genuine algorithmic combination of GLA-style consistency and geometric phase constraints, but the claimed convergence to the two candidates—the whole novelty—is an unsupported empirical observation and the measured gains are marginal. read the letter →

arxiv 2507.02192 v1 pith:WLOKSDEB submitted 2025-07-02 eess.AS

Chun-Wei Ho , Pin-Jui Ku , Hao Yen , Sabato Marco Siniscalchi , Yu Tsao , Chin-Hui Lee This is my paper · ORCID
classification eess.AS
keywords speechenhancementphaseestimationGriffin-LimalgorithmSTFTconsistencygeometricconstraintslawofsinessignambiguityadditivenoise
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

The paper sets out to show that the sign ambiguity in geometry-based phase estimation can be resolved by iteration instead of by a trained classifier. Its multi-source Griffin-Lim algorithm (MSGLA) alternates STFT-consistency projections between the clean and noise spectrograms, and the authors report that after about five iterations the estimated speech phase settles on one of the two candidates that the law of cosines allows, or on one of two candidates from a new law-of-sines formulation that uses the noise phase. If this convergence is dependable, the DNN-based sign predictor used in earlier geometry-based systems becomes unnecessary. In oracle experiments and on the VB-DMD and WSJ0-CHiME3 benchmarks, the method matches or slightly beats direct phase estimation and sign prediction, with its clearest advantage in background-noise suppression.

What carries the argument

The load-bearing object is the MSGLA alternating-projection loop, a sequence of three steps per iteration: a GLA-style consistency projection of the speech spectrogram, a GLA-style projection of the noise residual, and a geometric re-derivation of the speech phase from $H_Y - \hat{A}_Z e^{j\tilde{P}_Z}$ (NM-MSGLA) or from the law-of-sines relation (NP-MSGLA). The law-of-sines relation is the paper's new geometric ingredient: it turns known speech magnitude and noise phase into two candidate clean phases per time-frequency bin, a formulation the authors say has not been used before. The work of the loop is to convert sign selection from a classification problem into a fixed-point problem: the paper's claim is that the consistency constraints alone select the correct candidate, so the entire geometry-based pipeline reduces to a magnitude estimator followed by a small number of cheap projections.

What would settle it

Run NM-MSGLA from several random phase initializations on one fixed noisy utterance and record where the five-iteration output lands: if it ever lands away from $P_Y + |\Delta\hat{P}|$ and $P_Y - |\Delta\hat{P}|$, if the chosen candidate changes with initialization, or if continuing to fifty iterations changes the answer, then the claimed unsupervised sign resolution cannot be relied on.

Watch

Extended reading notes

Core claim

The paper's central claim is that the sign choice in Equation (4) — whether to add or subtract the absolute phase difference $|\Delta\hat{P}|$ — need not be learned. By alternately projecting the speech magnitude $\hat{A}_X$ and the noise residual $H_Y - \hat{A}_X e^{j\tilde{P}_X}$ through the STFT consistency operator $STFT \circ iSTFT$, and re-deriving the speech phase from the additive model $H_Y = H_X + H_Z$, the estimate $\hat{P}_X^{(n)}$ is observed to converge within a few iterations to either $P_Y + |\Delta\hat{P}|$ or $P_Y - |\Delta\hat{P}|$ (NM-MSGLA). The companion NP-MSGLA variant instead reconstructs phase from the law of sines: given the estimated speech magnitude and noise phase, the clean phase must be one of the two values $\sin^{-1}(\frac{A_Y}{\hat{A}_X}\sin(P_Y - \hat{P}_Z)) + \hat{P}_Z$ or $\pi - \sin^{-1}(\frac{A_Y}{\hat{A}_X}\sin(P_Y - \hat{P}_Z)) + \hat{P}_Z$, and the same alternating-projection mechanism selects between them. The authors report that with oracle magnitudes or oracle noise phase, phase reconstruction closely tracks the ground truth, and in blind settings MSGLA matches or slightly outperforms direct phase estimation, plain GLA, and the DNN-based sign predictor, with the largest gains on background-suppression metrics (SI-SNR and CBAK).

Load-bearing premise

The load-bearing premise is the empirical observation that after about five iterations the alternating projections converge to one of the two geometrically valid phase candidates; if the loop can oscillate, land on a spurious point, or depend on initialization, the unsupervised sign resolution that replaces the trained classifier gives way.

Editorial extensions

If this is right

  • In geometry-based phase estimation, the trained sign predictor can be dropped: roughly five alternating projections select the sign, removing the misclassification errors that near-random sign targets cause.
  • Noise phase becomes a usable cue for the first time: NP-MSGLA derives phase candidates from the law of sines, and it is most reliable in low-energy regions where clean speech phase estimation fails.
  • Oracle experiments indicate that noise magnitude accuracy matters more than speech magnitude accuracy for phase reconstruction, a result the paper highlights as pointing magnitude estimation toward the noise component.
  • On VB-DMD and WSJ0-CHiME3, both MSGLA variants match or slightly outperform direct phase estimation and the DNN sign predictor, with NP-MSGLA posting the best SI-SNR and CBAK scores on VB-DMD.

Reading between the lines

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

  • The convergence claim is empirical, so a fixed-point analysis of the alternating projections is the natural next step: a proof that the two geometric candidates are the only stable attractors would convert the method from an observation into a theorem.
  • The additive-model recipe generalizes to more than two components, so the same alternating-projection idea could attack sign ambiguity in multi-talker mixtures, which has the same binary structure per time-frequency bin.
  • NP-MSGLA suggests a simpler training objective for single-channel enhancement: a model that outputs a speech magnitude mask plus a noise phase estimate could replace full complex-spectrogram prediction.
  • Because STFT consistency depends on the analysis window, the number of iterations needed and the reliability of sign selection may vary with window length and hop size; a sweep over those settings would test whether the 'around five iterations' behavior holds regardless of the window.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The paper proposes an iterative phase estimation framework, MSGLA, for single-channel speech enhancement under additive noise. Two variants are introduced: NM-MSGLA, which alternates Griffin-Lim-style consistency projections on speech and noise spectrograms using estimated speech and noise magnitudes, and NP-MSGLA, which uses the estimated speech magnitude and an estimated noise phase together with the law of sines to construct phase candidates. The authors report oracle experiments on VB-DMD and blind evaluations on VB-DMD and WSJ0-CHiME3, comparing against a direct phase estimator, conventional GLA, and a DNN-based sign predictor. The central claimed contribution is that the iterative procedure converges, without a trained sign classifier, to one of the two geometrically valid phase candidates, thereby resolving the sign ambiguity of Eq. (4) in an unsupervised manner.

Significance. If the claimed unsupervised sign resolution is correct, the paper would offer a conceptually appealing way to combine geometric phase constraints with STFT consistency, potentially reducing the need for a separate DNN sign predictor. The oracle experiments are a useful contribution because they establish upper bounds and isolate the role of each estimated input. The paper also introduces a law-of-sines formulation for phase candidate construction that, to my knowledge, is new. However, the significance is currently limited by the absence of any theoretical or empirical verification of the central convergence mechanism, and by the small, unquantified performance differences in the blind evaluations. The manuscript is honest in reporting that convergence is an empirical observation, and the comparisons are fairly presented, but the load-bearing claim remains unsupported.

major comments (3)
  1. [Section 3.1, Eqs. (5)-(7) and Section 3.2, Eqs. (10)-(12)] The central claim of the paper, that MSGLA 'converges to either P_Y + |ΔP̂| or P_Y − |ΔP̂|' and thereby resolves the sign ambiguity, is only supported by the sentence 'Empirically, we observe...' and the analogous statement in Section 3.2. No fixed-point analysis, contraction argument, or stability condition is given, and the number of iterations is fixed at 5 with no sensitivity analysis. This is load-bearing because the entire novelty of the method rests on replacing the trained sign predictor with this iterative mechanism. As a concrete counterexample to the claim as stated, in the scalar case where the STFT projection is the identity, starting from P_X = P_Y with A_Y > A_X, A_Z, Eqs. (5)-(7) give P_Z = 0 and then P_X' = 0, so the iteration stalls at the noisy phase and does not converge to either geometric candidate. The STFT consistency coupling may prevent such stalling in practice, but the paper neither analyzes that coupling nor reports the fraction of time-frequency bins that actually converge to one of the two candidates, nor the fraction that select the correct branch. I request a proof or a precisely stated sufficient condition for convergence, together with per-bin convergence statistics over the test sets.
  2. [Section 3.2, Eqs. (8)-(9)] The law-of-sines formulation in Eq. (8) leads to the two candidate solutions in Eq. (9) only when |(A_Y / A_X) sin(P_Y − P_Z)| ≤ 1. In many time-frequency bins, particularly where the estimated speech magnitude is small or the phase difference is unfavorable, the argument of the inverse sine exceeds unity and the candidates do not exist. The paper does not state how such bins are handled in NP-MSGLA, nor whether this limitation affects the convergence behavior or the reported results. Since NP-MSGLA is one of the two proposed variants, this domain issue needs to be addressed explicitly, either by a fallback rule or by an analysis of how often the condition is violated on the test data.
  3. [Tables 2 and 3] The blind evaluation results show differences of only 0.01 to 0.05 in PESQ, ESTOI, SI-SNR, and CBAK between the proposed methods and the baselines, and some differences favor the baselines (e.g., NM-MSGLA PESQ 3.44 vs. GLA 3.45 on VB-DMD; NM-MSGLA SI-SNR 14.89 vs. direct phase estimator 15.88 on WSJ0-CHiME3). No confidence intervals, standard deviations, or significance tests are provided, and it is not stated how many utterances or random seeds are used. The conclusion that MSGLA 'matches well or slightly outperforms' existing algorithms, particularly in background suppression, is not statistically supported by the reported numbers. At minimum, the authors should report variance across test utterances and a paired significance test (e.g., Wilcoxon signed-rank) for the key comparisons.
minor comments (5)
  1. [Abstract and Section 1] The phrase 'multi-mource Griffin-Lim algorithm' in Section 3 contains a typo; it should be 'multi-source'.
  2. [Table 1 caption and Section 5.1] In the text, 'SI-NSR' should be 'SI-SNR', and 'NP-MSGL' should be 'NP-MSGLA'.
  3. [Section 5.1, Table 1] The cosine similarity values in Table 1 are reported only as averages. Since phase similarity is highly variable across frequency bins and utterances, reporting a standard deviation or a per-bin histogram would help the reader judge the consistency of the proposed method.
  4. [Section 5.2, sign predictor baseline] The sign predictor baseline is described as a separate compact TF-GridNet model with 219K parameters. It would be helpful to state explicitly whether this model is trained jointly with the magnitude estimator or independently, and to report the sign prediction accuracy on the test sets.
  5. [References] Reference [31] is a textbook and is cited for the law of sines and cosines; a more specific citation to a trigonometric identity or a prior phase-estimation paper would be more informative. Also, reference [22] is self-cited and is not yet published; the reader would benefit from a short explanation of its consistency-preserving loss and how it relates to the present work.

Circularity Check

0 steps flagged · score 2.0 of 10

No circular derivation: MSGLA's phase updates are fixed geometric/consistency projections, and no fitted parameter is renamed as a prediction.

full rationale

The paper's phase reconstruction chain is not circular. The NM-MSGLA updates (Eqs. 5-7) and NP-MSGLA updates (Eqs. 10-12) are explicit, parameter-free combinations of STFT consistency projections and trigonometric identities (law of cosines, law of sines). No parameter is fitted to the final phase and then reported as a prediction; the learned TF-GridNet outputs are magnitude and noise-phase estimates, which serve as fixed inputs to a deterministic iterative scheme. The law-of-cosines candidates (Eq. 3-4) and law-of-sines candidates (Eq. 9) are standard geometric consequences of the additive-noise model, not imported from the authors' prior work. Self-citations [22] and [19] appear in the introduction as motivational references for consistency and phase-aware enhancement, but the update equations themselves rely on GLA [33], consistency constraints [34], and the geometric phase formulation [27]-[31], all external works. The central claim that MSGLA converges to one of the two geometric candidates is asserted empirically in Sections 3.1 and 3.2 without fixed-point analysis; this is a rigor and validity risk (spurious fixed points may exist, and the chosen branch is not guaranteed to be the correct one), but it is not circularity because the convergence claim is not used to derive the algorithm. Oracle experiments use ground-truth magnitudes as inputs, which is a standard controlled evaluation and does not make the estimated phase equivalent to the target by construction. Overall, no prediction or first-principles result reduces to its own inputs, so the circularity score is low.

Assumptions & free parameters 1 free parameters · 3 assumptions · 0 invented entities

The method introduces no new physical entities. It relies on standard STFT analysis, additive noise, and estimated magnitudes/phases. The only hand-chosen parameter is the iteration count (L=5).

free parameters (1)
  • Iteration count L = 5
    Number of MSGLA iterations; chosen empirically, no sensitivity analysis provided (Section 5.1: 'each method is run for 5 iterations').
assumptions (3)
  • domain assumption Additive noise model HY = HX + HZ holds
    Stated in Section 2; all geometric constraints and updates rely on this.
  • standard math STFT consistency projection (STFT○iSTFT) is a valid constraint for speech and noise
    Assumed from GLA [33]; the algorithm alternates these projections.
  • domain assumption Magnitudes AX and AZ are available or accurately estimated
    NM-MSGLA requires both magnitude estimates; NP-MSGLA requires AX and PZ. Oracle experiments show sensitivity to estimation errors.

how reviews work

0 comments
Cite this review

Pith. "Pith review of An Investigation on Combining Geometry and Consistency Constraints into Phase Estimation for Speech Enhancement." pith.science (2026). https://pith.science/paper/WLOKSDEB

@misc{pith2026250702192,
  author       = {Pith},
  title        = {Pith review of: An Investigation on Combining Geometry and Consistency Constraints into Phase Estimation for Speech Enhancement},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WLOKSDEB}},
  note         = {Machine review of arXiv:2507.02192}
}
read the original abstract

We propose a novel iterative phase estimation framework, termed multi-source Griffin-Lim algorithm (MSGLA), for speech enhancement (SE) under additive noise conditions. The core idea is to leverage the ad-hoc consistency constraint of complex-valued short-time Fourier transform (STFT) spectrograms to address the sign ambiguity challenge commonly encountered in geometry-based phase estimation. Furthermore, we introduce a variant of the geometric constraint framework based on the law of sines and cosines, formulating a new phase reconstruction algorithm using noise phase estimates. We first validate the proposed technique through a series of oracle experiments, demonstrating its effectiveness under ideal conditions. We then evaluate its performance on the VB-DMD and WSJ0-CHiME3 data sets, and show that the proposed MSGLA variants match well or slightly outperform existing algorithms, including direct phase estimation and DNN-based sign prediction, especially in terms of background noise suppression.

Figures

Figures reproduced from arXiv: 2507.02192 by the authors.

Figure 1
Figure 1. A block diagram of the proposed MSGLA. The iSTFT-STFT [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 3
Figure 3. Illustration of inputs and outputs for the two MSGLA variants. [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figure 2
Figure 2. (a)(b) Comparison of noise and speech phase spectrogram [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

44 extracted references · 42 canonical work pages

  1. [1]

    Enhancement of speech corrupted by acoustic noise,

    M. Berouti, R. Schwartz, and J. Makhoul, “Enhancement of speech corrupted by acoustic noise,” in Proc. ICASSP, 1979

    work page 1979

  2. [2]

    Enhancement and bandwidth compression of noisy speech,

    J. Lim and A. Oppenheim, “Enhancement and bandwidth compression of noisy speech,” Proceedings of the IEEE , vol. 67, no. 12, pp. 1586–1604, 1979

    work page 1979

  3. [3]

    A speech enhancement method based on kalman filtering,

    K. Paliwal and A. Basu, “A speech enhancement method based on kalman filtering,” in Proc. ICASSP, 1987

    work page 1987

  4. [4]

    Speech segregation based on pitch tracking and amplitude modulation,

    G. Hu and D. Wang, “Speech segregation based on pitch tracking and amplitude modulation,” in Proc. WASPAA, 2001

    work page 2001

  5. [5]

    Binary and ratio time-frequency masks for robust speech recognition,

    S. Srinivasan, N. Roman, and D. Wang, “Binary and ratio time-frequency masks for robust speech recognition,” Speech Communication , vol. 48, no. 11, pp. 1486–1501, 2006

    work page 2006

  6. [6]

    A regression approach to speech enhancement based on deep neural networks,

    Y . Xu, J. Du, L.-R. Dai, and C.-H. Lee, “A regression approach to speech enhancement based on deep neural networks,” IEEE/ACM Trans. on Audio, Speech and Language Process. , vol. 23, no. 1, pp. 7–19, 2015

    work page 2015

  7. [7]

    A regression approach to single- channel speech separation via high-resolution deep neural networks,

    J. Du, Y . Tu, L.-R. Dai, and C.-H. Lee, “A regression approach to single- channel speech separation via high-resolution deep neural networks,” IEEE/ACM Trans. on Audio, Speech, and Language Process. , vol. 24, no. 8, pp. 1424–1437, 2016

    work page 2016

  8. [8]

    A reverberation-time-aware approach to speech dereverberation based on deep neural networks,

    B. Wu, K. Li, M. Yang, and C.-H. Lee, “A reverberation-time-aware approach to speech dereverberation based on deep neural networks,” IEEE/ACM Trans. on Audio, Speech, and Language Process. , vol. 25, no. 1, pp. 102–111, 2017

    work page 2017

Show all 44 references
  1. [9]

    On mean absolute error for deep neural network based vector-to-vector regression,

    J. Qi, J. Du, S. M. Siniscalchi, X. Ma, and C.-H. Lee, “On mean absolute error for deep neural network based vector-to-vector regression,” IEEE Signal Processing Letters , vol. 27, pp. 1485–1489, 2020

    2020

  2. [10]

    Vector-to-vector regression via distributional loss for speech enhancement,

    S. M. Siniscalchi, “Vector-to-vector regression via distributional loss for speech enhancement,” IEEE Signal Processing Letters , vol. 28, pp. 254–258, 2021

    2021

  3. [11]

    The importance of phase in speech enhancement,

    K. Paliwal, K. W ´ojcicki, and B. Shannon, “The importance of phase in speech enhancement,” speech communication, vol. 53, no. 4, pp. 465–494, 2011

    2011

  4. [12]

    On phase importance in parameter estimation in single-channel speech enhancement,

    P. Mowlaee and R. Saeidi, “On phase importance in parameter estimation in single-channel speech enhancement,” in Proc. ICASSP, 2013

    2013

  5. [13]

    Phase processing for single-channel speech enhancement: History and recent advances,

    T. Gerkmann, M. Krawczyk-Becker, and J. Le Roux, “Phase processing for single-channel speech enhancement: History and recent advances,” IEEE Signal Processing Magazine , vol. 32, no. 2, pp. 55–66, 2015

    2015

  6. [14]

    Phase-sensitive and recognition-boosted speech separation using deep recurrent neural networks,

    H. Erdogan, J. R. Hershey, S. Watanabe, and J. Le Roux, “Phase-sensitive and recognition-boosted speech separation using deep recurrent neural networks,” in Proc. ICASSP, 2015

    2015

  7. [15]

    Phase reconstruction from amplitude spectrograms based on von-mises- distribution deep neural network,

    S. Takamichi, Y . Saito, N. Takamune, D. Kitamura, and H. Saruwatari, “Phase reconstruction from amplitude spectrograms based on von-mises- distribution deep neural network,” in Proc. IWAENC, 2018

    2018

  8. [16]

    Divide and conquer: A deep casa approach to talker-independent monaural speaker separation,

    Y . Liu and D. Wang, “Divide and conquer: A deep casa approach to talker-independent monaural speaker separation,” IEEE/ACM Trans. on audio, speech, and language process. , vol. 27, no. 12, pp. 2092–2102, 2019

    2019

  9. [17]

    Learning complex spectral mapping with gated convolutional recurrent networks for monaural speech enhancement,

    K. Tan and D. Wang, “Learning complex spectral mapping with gated convolutional recurrent networks for monaural speech enhancement,” IEEE/ACM Trans. on Audio, Speech, and Language Process. , vol. 28, pp. 380–390, 2019

    2019

  10. [18]

    A multi-target snr-progressive learning approach to regression based speech enhancement,

    Y .-H. Tu, J. Du, T. Gao, and C.-H. Lee, “A multi-target snr-progressive learning approach to regression based speech enhancement,” IEEE/ACM Trans. on Audio, Speech, and Language Process. , vol. 28, pp. 1608–1619, 2020

    2020

  11. [19]

    A Multi-dimensional Deep Structured State Space Approach to Speech Enhancement Using Small-footprint Models,

    P.-J. Ku, C.-H. H. Yang, S. M. Siniscalchi, and C.-H. Lee, “A Multi-dimensional Deep Structured State Space Approach to Speech Enhancement Using Small-footprint Models,” in Proc. Interspeech, 2023

    2023

  12. [20]

    MP-SENet: A speech enhancement model with parallel denoising of magnitude and phase spectra,

    Y .-X. Lu, Y . Ai, and Z.-H. Ling, “MP-SENet: A speech enhancement model with parallel denoising of magnitude and phase spectra,” in Proc. Interspeech, 2023

    2023

  13. [21]

    Tf- gridnet: Making time-frequency domain models great again for monaural speaker separation,

    Z.-Q. Wang, S. Cornell, S. Choi, Y . Lee, B.-Y . Kim, and S. Watanabe, “Tf- gridnet: Making time-frequency domain models great again for monaural speaker separation,” in Proc. ICASSP, 2023

    2023

  14. [22]

    An explicit consistency-preserving loss function for phase reconstruction and speech enhancement,

    P.-J. Ku, C.-W. Ho, H. Yen, S. M. Siniscalchi, and C.-H. Lee, “An explicit consistency-preserving loss function for phase reconstruction and speech enhancement,” in Proc. ICASSP, 2025

    2025

  15. [23]

    Importance of window shape for phase-only reconstruction of speech,

    L. Alsteris and K. Paliwal, “Importance of window shape for phase-only reconstruction of speech,” in Proc. ICASSP, 2004

    2004

  16. [24]

    Phase estimation in single channel speech enhancement using phase decomposition,

    J. Kulmer and P. Mowlaee, “Phase estimation in single channel speech enhancement using phase decomposition,” IEEE Signal Processing Letters, vol. 22, no. 5, pp. 598–602, 2015

    2015

  17. [25]

    Phase reconstruction based on recurrent phase unwrapping with deep neural networks,

    Y . Masuyama, K. Yatabe, Y . Koizumi, Y . Oikawa, and N. Harada, “Phase reconstruction based on recurrent phase unwrapping with deep neural networks,” in Proc. ICASSP, 2020

    2020

  18. [26]

    Unre- stricted global phase bias-aware single-channel speech enhancement with conformer-based metric gan,

    S. Zhang, Z. Qiu, D. Takeuchi, N. Harada, and S. Makino, “Unre- stricted global phase bias-aware single-channel speech enhancement with conformer-based metric gan,” in Proc. ICASSP, 2024

    2024

  19. [27]

    Phase estimation for signal reconstruction in single-channel source separation,

    P. Mowlaee, R. Saeidi, and R. Martin, “Phase estimation for signal reconstruction in single-channel source separation,” in Proc. Interspeech, 2012

    2012

  20. [28]

    Deep learning based phase reconstruction for speaker separation: A trigonometric perspective,

    Z.-Q. Wang, K. Tan, and D. Wang, “Deep learning based phase reconstruction for speaker separation: A trigonometric perspective,” in Proc. ICASSP, 2019

    2019

  21. [29]

    Iterative closed-loop phase-aware single- channel speech enhancement,

    P. Mowlaee and R. Saeidi, “Iterative closed-loop phase-aware single- channel speech enhancement,” IEEE Signal Processing Letters , vol. 20, no. 12, pp. 1235–1239, 2013

    2013

  22. [30]

    Time-frequency constraints for phase estimation in single-channel speech enhancement,

    ——, “Time-frequency constraints for phase estimation in single-channel speech enhancement,” in Proc. IWAENC, 2014

    2014

  23. [31]

    Stewart, L

    J. Stewart, L. Redlin, and S. Watson, Precalculus: Mathematics for Calculus, 7th ed. Cengage Learning, 2015

    2015

  24. [32]

    Leveraging low-distortion target estimates for improved speech enhancement,

    Z.-Q. Wang, G. Wichern, and J. L. Roux, “Leveraging low-distortion target estimates for improved speech enhancement,” arXiv preprint arXiv:2110.00570, 2021

    2021 arXiv

  25. [33]

    Signal estimation from modified short-time fourier transform,

    D. Griffin and J. Lim, “Signal estimation from modified short-time fourier transform,” IEEE/ACM Trans. on Audio, Speech, and Language Process. , vol. 32, no. 2, pp. 236–243, 1984

    1984

  26. [34]

    Explicit consistency constraints for stft spectrograms and their application to phase reconstruction

    J. Le Roux, N. Ono, and S. Sagayama, “Explicit consistency constraints for stft spectrograms and their application to phase reconstruction.” in Proc. Interspeech, 2008

    2008

  27. [35]

    Noisy speech database for training speech enhancement algorithms and tts models,

    C. Valentini-Botinhao, “Noisy speech database for training speech enhancement algorithms and tts models,” 2017

    2017

  28. [36]

    StoRM: A Diffusion-based Stochastic Regenera- tion Model for Speech Enhancement and Dereverberation,

    J.-M. Lemercier et al. , “StoRM: A Diffusion-based Stochastic Regenera- tion Model for Speech Enhancement and Dereverberation,” IEEE/ACM Trans. on Audio, Speech, and Language Process. , vol. 31, pp. 2724–2737, 2023

    2023

  29. [37]

    Conditional diffusion probabilistic model for speech enhancement,

    Y .-J. Lu et al. , “Conditional diffusion probabilistic model for speech enhancement,” in Proc. ICASSP, 2022

    2022

  30. [38]

    Real time speech enhancement in the waveform domain,

    A. D ´efossez, G. Synnaeve, and Y . Adi, “Real time speech enhancement in the waveform domain,” in Proc. Interspeech, 2020

    2020

  31. [39]

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

    A. Rix, J. Beerends, M. Hollier, and A. Hekstra, “Perceptual evaluation of speech quality (PESQ) - a new method for speech quality assessment of telephone networks and codecs,” in Proc. ICASSP, 2001

    2001

  32. [40]

    An algorithm for predicting the intelligibility of speech masked by modulated noise maskers,

    J. Jensen and C. H. Taal, “An algorithm for predicting the intelligibility of speech masked by modulated noise maskers,” IEEE/ACM Trans. on Audio, Speech, and Language Process. , vol. 24, no. 11, pp. 2009–2022, 2016

    2009

  33. [41]

    SDR – Half- baked or Well Done?

    J. L. Roux, S. Wisdom, H. Erdogan, and J. R. Hershey, “SDR – Half- baked or Well Done?” in Proc. ICASSP, 2019

    2019

  34. [42]

    Evaluation of objective quality measures for speech enhancement,

    Y . Hu and P. C. Loizou, “Evaluation of objective quality measures for speech enhancement,” IEEE Trans. on Audio, Speech, and Language Process., vol. 16, no. 1, pp. 229–238, 2008

    2008

  35. [43]

    Adam: A method for stochastic optimization,

    D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 , 2017

    2017 arXiv

  36. [44]

    MetricGAN: Generative adversarial networks based black-box metric scores optimization for speech enhancement,

    S.-W. Fu, C.-F. Liao, Y . Tsao, and S.-D. Lin, “MetricGAN: Generative adversarial networks based black-box metric scores optimization for speech enhancement,” in Proc. ICML, 2019

    2019

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.