Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation

Chao Pan

REVIEW 3 major objections 4 minor 80 references

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation

T0 review · 3 major / 4 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read This review argues that all data-driven acoustic signal processing reduces to three task families and two design choices: which network modules to compose and which loss function to attach.

desk verdict A competent, openly selective tutorial of data-driven acoustic signal processing; useful as a teaching map but not a research contribution, and the 'comprehensive' framing oversells the coverage. read the letter →

arxiv 2508.21470 v1 pith:VMTQ4PYS submitted 2025-08-29 eess.AS

Chao Pan This is my paper · ORCID

classification eess.AS

keywords data-drivenacousticsignalprocessingdeeplearninglossfunctionconstructiondetectionfilteringtransformationweak-labelvoiceprintextraction

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 tries to establish a single organizing map for data-driven acoustic signal processing: however diverse the applications look, every deep-learning method is assembled from two design decisions — which network modules to compose (convolution, recurrent, residual, U-Net, attention, transformer, and so on) and which loss function to attach. Against that two-lever picture, the paper sorts the field into three task families — transformation, detection, and filtering — and shows that five canonical acoustic problems (sound source localization, sound event detection, voiceprint extraction, noise reduction, and source separation) are instances of those families. The map matters because it turns a scattered literature into a recipe book: classify the task, and the family of output layers and loss constructions is largely fixed, with concrete devices such as turning clip-level weak labels into frame-level detection probabilities through non-uniform aggregation. The paper is explicitly a pedagogical summary rather than a report of new experimental results, and the author states it provides no mathematical proofs.

What carries the argument

Two mechanisms carry the argument. Composite-function notation writes every network as f(x) = S ◦ [modules](x) over a small alphabet (C convolution, G recurrent, R residual, U U-Net, E/D encoder-decoder, A aggregation, F fully connected, T transformer): modules define a function space, the loss selects the parameters. A loss taxonomy keys each task family to its losses — cross-entropy types for detection behind sigmoid/softmax outputs; MSE, ℓ1, and Huber for filtering; contrastive, triplet, and adversarial density alignment for label-free transformation. The emblematic identity is the linear softmax aggregation ˆy = Σŷ²/Σŷ, collapsing frame-level detection probabilities to a clip-level estim

What would settle it

Take one year of published audio deep-learning papers and check whether every method decomposes into the listed network modules with a loss from the listed families: a substantial method outside the scheme would refute the 'comprehensive framework' claim. For the technical recipe, train a weak-label sound event detection model with the linear softmax aggregation and test the paper's prediction that frame-level probabilities converge toward 0 and 1 rather than staying diffuse.

Watch

Extended reading notes

Core claim

Central claim: the diversity of data-driven acoustic signal processing collapses into a small construction grammar. Networks are composite functions of named modules; the objective function selects the right mapping. What differs between tasks is the loss: detection uses cross-entropy behind sigmoid/softmax outputs; filtering uses norm distances (MSE, ℓ1, Huber); transformation uses clustering (contrastive, triplet) or density alignment (adversarial/Wasserstein). The five canonical problems fall out: localization and event detection are detection; denoising and separation are filtering; voiceprint extraction is transformation, because test-time speakers are unseen. The emblematic recipe is n

Load-bearing premise

The claim to be a comprehensive map assumes that the five selected acoustic problems and the module/loss taxonomy really do cover the core of the field; the author states the paper supplies no mathematical proofs and that the surveyed networks are merely recent reported results, not demonstrated best performers.

Editorial extensions

If this is right

Classify a new acoustic task as transformation, detection, or filtering and the output-layer and loss-family choices are largely fixed, so the framework doubles as a design guide rather than only a taxonomy.
Weak clip-level labels can be upgraded to frame-level detection probabilities using non-uniform weighted aggregation — the Σŷ²/Σŷ construction — without collecting frame-level annotations.
Class imbalance in detection (rare events, rare speakers) is handled on the loss side with a menu of interchangeable devices: weighted binary cross-entropy, inverse frequency loss, asymmetric focal weighting, and Dice-type losses.
Voiceprint recognition is best framed as signal transformation rather than classification, because enrollment speakers are unseen at training time; the clustering/density losses and data-augmentation robustness follow from that reframing.
One shared feature extractor can serve several objectives at once — source counting and source direction, or sound event detection and scene classification — by branching into multiple output heads with different losses.

Reading between the lines

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

The module-by-loss recipe space suggests a missing piece the paper does not pursue: a systematic benchmark that runs one dataset through combinations of modules and losses, so the taxonomy would predict performance regimes rather than only describe methods.
The sparsity-promoting aggregation (Σŷ²/Σŷ) is one member of a family of soft top-k pooling operators; a natural testable extension is whether learnable smooth top-k or entropy-regularized variants improve weakly supervised detection beyond the closed-form version.
The detection/filtering/transformation trichotomy is stated for acoustics, but the loss taxonomy it rests on is signal-agnostic; an implicit claim waiting to be tested is that the same map organizes data-driven processing of seismic, biomedical, or radar signals.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

This is a survey/tutorial, not a research preprint, and it says so itself: Section 1.5 disclaims mathematical proofs, Section 4.3 says the reviewed networks are merely reported results and not claimed to outperform others. I would not referee it as a novel result, but it deserves a serious look as a teaching text.

What it does well: the mathematics is standard and, where I checked, correct. Backpropagation, Adam, FIR cascade receptive fields, IIR-to-FIR expansion, LDA eigen-solution, and STFT analysis/filter/reconstruction are all presented accurately. The composite-function module notation (C, R, U, F, T, etc.) is genuinely helpful for organizing a large field, and the CNN-as-FIR / RNN-as-IIR analogy is a nice explanatory device. The organization by task family (detection, filtering, transformation) and by the two design levers (network modules and loss functions) gives a newcomer a usable mental map. The five worked problems—localization, sound event detection, voiceprint, noise reduction, separation—are the right backbone for an acoustic-signal-processing audience.

Soft spots, in proportion. The word 'comprehensive' in the title and abstract is the weakest part. The paper explicitly limits itself and gives no coverage criterion, so the chosen taxonomy and five problems are a selective snapshot rather than a validated organizing map. Some important areas, such as acoustic scene classification, diarization, echo cancellation, and dereverberation, are absent or only touched on. That is fine for a focused tutorial, but it should not claim comprehensiveness. There are also minor typos in equations (122)-(123) and (218)-(219); the section 9.3 claim that AUC is the 'optimal optimization target' is unproved and uncited; and the linear softmax weighted aggregation construction appears twice (Sections 3.1.4 and 5.2.5), which is repetition, not a flaw. The literature coverage is selective, and I cannot verify fidelity to all ~50 cited papers, but the citations I know are used appropriately. No code or data is shipped, which is normal for a review and not a defect.

Bottom line: this is a useful, honest tutorial for newcomers and perhaps for course designers. It is not a research contribution, and the 'comprehensive' framing should be softened. I would send it to peer review as a survey/tutorial, with a request that the authors fix the typos, tone down the overclaim, and either add a coverage criterion or explicitly label the selection as illustrative. Set would_accept_peer_review=true.

Referee Report

3 major / 4 minor

Summary. This paper presents itself as a systematic summary and a "comprehensive understanding framework" for data-driven acoustic signal processing. It organizes the field into three task families (transformation, detection, filtering) and five canonical problems (source localization, sound event detection, voiceprint extraction/recognition, noise reduction, source separation). The first half develops notation for neural network modules, derives basic backpropagation and the Adam update, and surveys loss-construction principles for detection, estimation, and transformation. The second half gives per-problem signal models, feature representations, loss functions, and representative network architectures, followed by a final section on GANs, optimal transport, AUC optimization, diffusion models, and visualization. The paper is written as a tutorial: it emphasizes conceptual structure and mathematical notation rather than experimental comparisons, literature surveys, or proofs.

Significance. If the taxonomy and derivations are taken as a pedagogical map, the paper has real value: it connects classical signal-processing concepts (FIR/IIR filtering, STFT analysis-filter-reconstruction, Wiener filtering ideas, LDA) with modern neural-network design patterns and loss constructions. Many displayed derivations are standard and appear correct, including backpropagation (Eqs. 18–22), Adam (Eq. 28), FIR cascade length (Eq. 48), IIR-to-FIR expansion (Eq. 50), and the sparse-vs-equal behavior of the softmax-weighted aggregation (Eqs. 124–125). The paper also has a strength in its clear modular notation, which could help practitioners and students navigate the area. However, the central claim of "comprehensiveness" is not substantiated, and at least one displayed mathematical result in a core section is stated incorrectly. With appropriate reframing and corrections, the paper could serve as a useful conceptual survey, but in its current form the mismatch between the abstract and the actual coverage, together with the technical error in Eq. (154), prevents recommending acceptance.

major comments (3)

[Abstract and §1.5, §4.3] The paper's central claim is that it provides a "comprehensive understanding framework" for data-driven acoustic signal processing. This claim is under-supported. Section 1.5 explicitly disclaims mathematical proofs, and Section 4.3 says the reviewed networks are "merely results reported in recent years and do not imply that they outperform other methods," but no selection criterion is given for the five canonical problems or for the two design levers (network modules, loss functions). Several problem families central to the field appear only in passing: acoustic scene classification is only a branch in §5.4.4, speaker diarization is mentioned in §6.1 but not developed, and echo cancellation/dereverberation appear only as passing references (e.g., §3.2.3). On the loss-function side, Section 3 omits major families such as domain-adversarial losses, deep clustering objectives for separatio
[§3.3.1, Eq. (154)] The LDA objective is displayed incorrectly. In the text, J_{3,1} is defined (Eq. 146) as the intra-class dispersion tr(A\Phi_w A^H), which is to be minimized, while J_{3,2} (Eq. 150) is the inter-class dispersion tr(A\Phi_b A^H), which is to be maximized. Eq. (154) writes the objective as max_A J_{3,1}/J_{3,2}, but the right-hand side is tr(A\Phi_b A^H)/tr(A\Phi_w A^H), which is J_{3,2}/J_{3,1}. The left-hand ratio must be J_{3,2}/J_{3,1} (or the definitions of J_{3,1} and J_{3,2} must be swapped). As written, the equation instructs the reader to maximize the wrong ratio, undermining a central derivation in the signal-transformation chapter.
[§7.1, Eq. (323)] The constraint on the window function for overlap-add reconstruction is described inconsistently. The text says that the "infinite number of equations" can be condensed into "L_w equations" when L_w = Q L_s, but then states that A is an L_s × L_w matrix. Since the constraint \sum_i \psi(t - i L_s) = 1 is periodic with period L_s, there are only L_s independent equations, not L_w. Please correct the sentence to "L_s equations" and make the dimensions of A consistent.

minor comments (4)

[§5.2.3, Eq. (244)] The weighted sorting aggregation uses λ^{t-1} with t = 0, giving a factor λ^{-1} in the first term. The limiting behavior as λ→0 indeed recovers max aggregation, but the expression is undefined at λ=0 and the index convention is nonstandard. Please clarify the allowed range (e.g., 0<λ<1) and explain the limit, or rewrite with a more standard index (e.g., λ^t with sorted descending order).
[§3.3.4, Eq. (143)] The notation J_{2.4} uses a dot where other losses use a comma (e.g., J_{2,1}, J_{2,2}). Please unify the notation.
[§6.3.1, Eq. (307)] The aggregation output is written as a column vector (μ ; σ), which is fine, but the preceding description of the standard deviation computation could be misread as a scalar standard deviation. Clarify that μ and σ are vectors computed element-wise across the 1500 channels.
[Throughout] The reference numbers [1]–[50] are cited without a visible bibliography in the provided text, making it impossible for a reader to verify attributions. Ensure the final manuscript includes a complete reference list with titles and venues.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: survey/tutorial with no fitted-to-prediction chain; the taxonomy is an organizing claim, not a derived prediction.

full rationale

This is a survey/tutorial that makes no predictive or fitted-to-data claims; it organizes known material into a taxonomy (transformation/detection/filtering, network-module/loss-function levers, and five acoustic problems). The only internally repeated construction is the linear softmax-weighted aggregation / detection-probability super-resolution formula (Eqs. 119-123 and 247-251), but it is presented as a cited method with an explicit mathematical motivation (h = sum y^2 / sum y encourages sparsity), not as evidence for the paper's organizational claim, and the survey's central claim does not depend on it. The paper explicitly disclaims completeness and proofs (Sec. 1.5; Sec. 4.3), which weakens the 'comprehensive' framing as a coverage matter but does not create a circular derivation. No parameter is fitted and then called a prediction; no uniqueness theorem or central premise rests on a self-citation chain. Thus no circular step is present.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The paper makes no fitted predictions and introduces no entities, so the ledger records only background assumptions that the pedagogical framework leans on. Free parameters: none are introduced by this paper; hyperparameters appearing in surveyed methods (weight alpha=100 in eq. 219, beta=100 in eq. 254, decision thresholds epsilon_g=0.5, epsilon_low=0.2, epsilon_high=0.75 in Section 5.1.2, margins zeta_0, focal exponents eta) are part of the described literature methods, not of a claim this paper makes. Invented entities: none; module symbols C(.), G(.), R(.), U(.), etc. are notation, not postulates. Axioms listed are the unproved background the survey silently relies on: standard gradient-based learning, the far-field direct-path microphone model, short-term stationarity with overlap-add reconstruction, and the heuristic validity of weak-label aggregation.

assumptions (4)

standard math Gradient-based optimization of differentiable loss functions on neural networks finds usable task solutions; backpropagation computes the required gradients (Sections 1.3-1.4).
The entire tutorial assumes this standard ML background; no convergence or approximation guarantees are stated.
domain assumption Microphone observations follow the direct-path-dominated linear model p_m(t) = h_0 s(t - tau_m) with plane-wave delays tau_m - tau_1 = delta_m,1 cos(theta)/c (Section 4.1, eqs. 172-177).
This is the standard far-field free-field array model; it is a simplification of real reverberant acoustics, appropriate for a tutorial but an assumption.
domain assumption Speech is short-term stationary so framing and overlap-add reconstruction with sum psi(t - i*L_s) = 1 is lossless (Section 7.1, eqs. 320-323).
Standard DSP assumption invoked for all frame-based processing in the review.
domain assumption Under weak labels, aggregating frame-level estimates (max, mean, softmax-weighted, or linear softmax-weighted) and training with binary cross-entropy yields meaningful frame-level detection probabilities (Sections 3.1.4, 5.2-5.3).
This is the paper's main substantive methodological claim and it is presented as literature practice [22], not derived; it is also where the paper's own super-resolution analysis (eqs. 124-125) is heuristic.

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Cite this review

Pith. "Pith review of Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation." pith.science (2026). https://pith.science/paper/VMTQ4PYS

@misc{pith2026250821470,
  author       = {Pith},
  title        = {Pith review of: Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VMTQ4PYS}},
  note         = {Machine review of arXiv:2508.21470}
}

read the original abstract

In recent decades, the field of signal processing has rapidly evolved due to diverse application demands, leading to a rich array of scientific questions and research areas. The forms of signals, their formation mechanisms, and the information extraction methods vary by application, resulting in diverse signal processing techniques. Common techniques can be categorized into three types: transformation, detection, and filtering. Signal transformation converts signals from their original domain to a more suitable target domain for analysis; signal detection aims to identify the existence of relevant information within a signal and its specific time and location; and signal filtering focuses on extracting or separating source signals of interest from observed signals. In acoustic signal processing, techniques include sound source localization, sound event detection, voiceprint extraction and recognition, noise reduction, and source separation, with applications in speech communication, voice interaction, smart healthcare, and industrial diagnostics. Recently, the advancement of deep learning technologies has shifted methodologies in acoustic signal processing from knowledge-driven to data-driven approaches, leading to significant research outcomes. This paper aims to systematically summarize the principles and methods of data-driven acoustic signal processing, providing a comprehensive understanding framework for academic exploration and practical applications.

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Reference graph

Works this paper leans on

80 extracted references · 65 canonical work pages

[1]

Yu and L

D. Yu and L. Deng, Automatic speech recognition. Springer, 2016, vol. 1

work page 2016
[2]

Ro- bust stochastic approximation approach to stochastic pro- gramming,

A. Nemirovski, A. Juditsky, G. Lan, and A. Shapiro, “Ro- bust stochastic approximation approach to stochastic pro- gramming,” SIAM Journal on optimization, vol. 19, no. 4, pp. 1574–1609, 2009

work page 2009
[3]

ADAM: A method for stochastic optimization,

D. P. Kingma and J. Ba, “ADAM: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980, 2014

arXiv 2014
[4]

Comparison of parametric rep- resentations for monosyllabic word recognition in continu- ously spoken sentences,

S. Davis and P. Mermelstein, “Comparison of parametric rep- resentations for monosyllabic word recognition in continu- ously spoken sentences,” IEEE transactions on acoustics, speech, and signal processing , vol. 28, no. 4, pp. 357–366, 1980

work page 1980
[5]

Efficient backprop,

Y . LeCun, L. Bottou, G. B. Orr, and K.-R. M ¨uller, “Efficient backprop,” inNeural networks: Tricks of the trade. Springer, 2002, pp. 9–50

work page 2002
[6]

Rectified linear units improve restricted boltzmann machines,

V . Nair and G. E. Hinton, “Rectified linear units improve restricted boltzmann machines,” in Proceedings of the 27th international conference on machine learning (ICML-10) , 2010, pp. 807–814

2010
[7]

Rectifier non- linearities improve neural network acoustic models,

A. L. Maas, A. Y . Hannun, A. Y . Ng et al., “Rectifier non- linearities improve neural network acoustic models,” inProc. ICML, vol. 30, no. 1. Atlanta, GA, 2013, p. 3

work page 2013
[8]

Searching for ac- tivation functions,

P. Ramachandran, B. Zoph, and Q. V . Le, “Searching for ac- tivation functions,” arXiv preprint arXiv:1710.05941, 2017. 56

arXiv 2017

Show all 80 references

[9]

Gradient- based learning applied to document recognition,

Y . LeCun, L. Bottou, Y . Bengio, and P. Haffner, “Gradient- based learning applied to document recognition,” Proceed- ings of the IEEE, vol. 86, no. 11, pp. 2278–2324, 1998

1998
[10]

Long short-term mem- ory,

S. Hochreiter and J. Schmidhuber, “Long short-term mem- ory,”Neural computation, vol. 9, no. 8, pp. 1735–1780, 1997

1997
[11]

Learning phrase representations using RNN encoder-decoder for statistical machine translation,

K. Cho, B. Van Merri ¨enboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y . Bengio, “Learning phrase representations using RNN encoder-decoder for statistical machine translation,” arXiv preprint arXiv:1406.1078, 2014

2014 arXiv
[12]

Deep residual learning for image recognition,

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE confer- ence on computer vision and pattern recognition , 2016, pp. 770–778

2016
[13]

Auto-encoding variational bayes,

D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” arXiv preprint arXiv:1312.6114, 2013

2013 arXiv
[14]

U-net: Convo- lutional networks for biomedical image segmentation,

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convo- lutional networks for biomedical image segmentation,” in International conference on medical image computing and computer-assisted intervention (MICCAI), Munich, Germany, October 5-9, 2015. Springer, 2015, pp. 234–241

2015
[15]

Batch normalization: Accelerating deep network training by reducing internal covariate shift,

S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in International conference on machine learning. pmlr, 2015, pp. 448–456

2015
[16]

Layer normalization,

J. L. Ba, J. R. Kiros, and G. E. Hinton, “Layer normalization,” arXiv preprint arXiv:1607.06450, 2016

2016 arXiv
[17]

Dropout: a simple way to prevent neural networks from overfitting,

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” The journal of machine learning research, vol. 15, no. 1, pp. 1929–1958, 2014

1929
[18]

Imagenet classification with deep convolutional neural networks,

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Ad- vances in neural information processing systems , vol. 25, 2012

2012
[19]

Attention is all you need,

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing sys- tems, vol. 30, 2017

2017
[20]

Conformer: Convolution-augmented transformer for speech recognition,

A. Gulati, J. Qin, C.-C. Chiu, N. Parmar, Y . Zhang, J. Yu, W. Han, S. Wang, Z. Zhang, Y . Wu et al. , “Conformer: Convolution-augmented transformer for speech recognition,” arXiv preprint arXiv:2005.08100, 2020

2005 arXiv
[21]

V-net: Fully con- volutional neural networks for volumetric medical image seg- mentation,

F. Milletari, N. Navab, and S.-A. Ahmadi, “V-net: Fully con- volutional neural networks for volumetric medical image seg- mentation,” in2016 fourth international conference on 3D vi- sion (3DV). Ieee, 2016, pp. 565–571

2016
[22]

A comparison of five multi- ple instance learning pooling functions for sound event de- tection with weak labeling,

Y . Wang, J. Li, and F. Metze, “A comparison of five multi- ple instance learning pooling functions for sound event de- tection with weak labeling,” in IEEE International Confer- ence on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2019, pp. 31–35

2019
[23]

Support-vector networks,

C. Cortes and V . Vapnik, “Support-vector networks,” Ma- chine learning, vol. 20, pp. 273–297, 1995

1995
[24]

Training a support vector machine in the pri- mal,

O. Chapelle, “Training a support vector machine in the pri- mal,” Neural computation , vol. 19, no. 5, pp. 1155–1178, 2007

2007
[25]

Robust truncated hinge loss support vector machines,

Y . Wu and Y . Liu, “Robust truncated hinge loss support vector machines,” Journal of the American Statistical Association , vol. 102, no. 479, pp. 974–983, 2007

2007
[26]

Robust estimation of a location parameter,

P. J. Huber, “Robust estimation of a location parameter,” in Breakthroughs in statistics: Methodology and distribution . Springer, 1992, pp. 492–518

1992
[27]

Ro- bust extended multidelay filter and double-talk detector for acoustic echo cancellation,

H. Buchner, J. Benesty, T. Gansler, and W. Kellermann, “Ro- bust extended multidelay filter and double-talk detector for acoustic echo cancellation,” IEEE Transactions on Audio, Speech, and Language Processing, vol. 14, no. 5, pp. 1633– 1644, 2006

2006
[28]

The use of multiple measurements in taxo- nomic problems,

R. A. Fisher, “The use of multiple measurements in taxo- nomic problems,” Annals of eugenics, vol. 7, no. 2, pp. 179– 188, 1936

1936
[29]

Fully-convolutional siamese networks for object tracking,

L. Bertinetto, J. Valmadre, J. F. Henriques, A. Vedaldi, and P. H. Torr, “Fully-convolutional siamese networks for object tracking,” in Computer Vision–ECCV 2016 Workshops: Am- sterdam, The Netherlands, 2016. Springer, 2016, pp. 850– 865

2016
[30]

Facenet: A uni- fied embedding for face recognition and clustering,

F. Schroff, D. Kalenichenko, and J. Philbin, “Facenet: A uni- fied embedding for face recognition and clustering,” in Pro- ceedings of the IEEE conference on computer vision and pat- tern recognition, 2015, pp. 815–823

2015
[31]

A simple framework for contrastive learning of visual representations,

T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, “A simple framework for contrastive learning of visual representations,” in International conference on machine learning . PMLR, 2020, pp. 1597–1607

2020
[32]

Momentum contrast for unsupervised visual representation learning,

K. He, H. Fan, Y . Wu, S. Xie, and R. Girshick, “Momentum contrast for unsupervised visual representation learning,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 9729–9738

2020
[33]

Self- supervised text-independent speaker verification using pro- totypical momentum contrastive learning,

W. Xia, C. Zhang, C. Weng, M. Yu, and D. Yu, “Self- supervised text-independent speaker verification using pro- totypical momentum contrastive learning,” in IEEE Interna- tional Conference on Acoustics, Speech and Signal Process- ing (ICASSP). IEEE, 2021, pp. 6723–6727

2021
[34]

Wasserstein gen- erative adversarial networks,

M. Arjovsky, S. Chintala, and L. Bottou, “Wasserstein gen- erative adversarial networks,” in International conference on machine learning. PMLR, 2017, pp. 214–223

2017
[35]

Improved training of wasserstein GANs,

I. Gulrajani, F. Ahmed, M. Arjovsky, V . Dumoulin, and A. C. Courville, “Improved training of wasserstein GANs,” Ad- vances in neural information processing systems , vol. 30, 2017. 57

2017
[36]

Performance study of the MVDR beamformer as a function of the source incidence an- gle,

C. Pan, J. Chen, and J. Benesty, “Performance study of the MVDR beamformer as a function of the source incidence an- gle,” IEEE/ACM Transactions on Audio, Speech, and Lan- guage Processing, vol. 22, no. 1, pp. 67–79, 2014

2014
[37]

ACCDOA: Activity-coupled cartesian direc- tion of arrival representation for sound event localization and detection,

K. Shimada, Y . Koyama, N. Takahashi, S. Takahashi, and Y . Mitsufuji, “ACCDOA: Activity-coupled cartesian direc- tion of arrival representation for sound event localization and detection,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . ...

2021
[38]

A deep learning framework for robust DOA estimation using spherical har- monic decomposition,

V . Varanasi, H. Gupta, and R. M. Hegde, “A deep learning framework for robust DOA estimation using spherical har- monic decomposition,” IEEE/ACM Transactions on Audio, Speech, and Language Processing , vol. 28, pp. 1248–1259, 2020

2020
[39]

Ro- bust source counting and doa estimation using spatial pseudo- spectrum and convolutional neural network,

T. N. T. Nguyen, W.-S. Gan, R. Ranjan, and D. L. Jones, “Ro- bust source counting and doa estimation using spatial pseudo- spectrum and convolutional neural network,” IEEE/ACM Transactions on Audio, Speech, and Language Processing , vol. 28, pp. 2626–2637, 2020

2020
[40]

Multi-microphone speaker separation based on deep DOA estimation,

S. E. Chazan, H. Hammer, G. Hazan, J. Goldberger, and S. Gannot, “Multi-microphone speaker separation based on deep DOA estimation,” in 27th European Signal Processing Conference (EUSIPCO). IEEE, 2019, pp. 1–5

2019
[41]

Towards duration robust weakly supervised sound event detection,

H. Dinkel, M. Wu, and K. Yu, “Towards duration robust weakly supervised sound event detection,”IEEE/ACM Trans- actions on Audio, Speech, and Language Processing, vol. 29, pp. 887–900, 2021

2021
[42]

Impact of sound duration and inactive frames on sound event detection performance,

K. Imoto, S. Mishima, Y . Arai, and R. Kondo, “Impact of sound duration and inactive frames on sound event detection performance,” in IEEE International Conference on Acous- tics, Speech and Signal Processing (ICASSP). IEEE, 2021, pp. 860–864

2021
[43]

Sound event detection and time-frequency segmentation from weakly labelled data,

Q. Kong, Y . Xu, I. Sobieraj, W. Wang, and M. D. Plumb- ley, “Sound event detection and time-frequency segmentation from weakly labelled data,” IEEE/ACM Transactions on Au- dio, Speech, and Language Processing , vol. 27, no. 4, pp. 777–787, 2019

2019
[44]

Weakly-supervised sound event detection with self-attention,

K. Miyazaki, T. Komatsu, T. Hayashi, S. Watanabe, T. Toda, and K. Takeda, “Weakly-supervised sound event detection with self-attention,” in ICASSP 2020-2020 IEEE Interna- tional Conference on Acoustics, Speech and Signal Process- ing (ICASSP). IEEE, 2020, pp. 66–70

2020
[45]

Sound event detection by multitask learning of sound events and scenes with soft scene labels,

K. Imoto, N. Tonami, Y . Koizumi, M. Yasuda, R. Yaman- ishi, and Y . Yamashita, “Sound event detection by multitask learning of sound events and scenes with soft scene labels,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020, pp...

2020
[46]

Speaker recognition based on deep learning: An overview,

Z. Bai and X.-L. Zhang, “Speaker recognition based on deep learning: An overview,” Neural Networks, vol. 140, pp. 65– 99, 2021

2021
[47]

Front-end factor analysis for speaker verification,

N. Dehak, P. J. Kenny, R. Dehak, P. Dumouchel, and P. Ouel- let, “Front-end factor analysis for speaker verification,”IEEE Transactions on Audio, Speech, and Language Processing , vol. 19, no. 4, pp. 788–798, 2010

2010
[48]

X-vectors: Robust DNN embeddings for speaker recognition,

D. Snyder, D. Garcia-Romero, G. Sell, D. Povey, and S. Khu- danpur, “X-vectors: Robust DNN embeddings for speaker recognition,” in IEEE international conference on acoustics, speech and signal processing (ICASSP) . IEEE, 2018, pp. 5329–5333

2018
[49]

Data augmentation using deep generative models for embedding based speaker recognition,

S. Wang, Y . Yang, Z. Wu, Y . Qian, and K. Yu, “Data augmentation using deep generative models for embedding based speaker recognition,” IEEE/ACM Transactions on Au- dio, Speech, and Language Processing , vol. 28, pp. 2598– 2609, 2020

2020
[50]

Target speaker verification with selective auditory attention for single and multi-talker speech,

C. Xu, W. Rao, J. Wu, and H. Li, “Target speaker verification with selective auditory attention for single and multi-talker speech,” IEEE/ACM Transactions on audio, speech, and lan- guage processing, vol. 29, pp. 2696–2709, 2021

2021
[51]

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

Y . Luo and N. Mesgarani, “Conv-TasNet: Surpassing ideal time–frequency magnitude masking for speech separation,” IEEE/ACM transactions on audio, speech, and language pro- cessing, vol. 27, no. 8, pp. 1256–1266, 2019

2019
[52]

Pearson correlation coefficient,

I. Cohen, Y . Huang, J. Chen, J. Benesty, J. Benesty, J. Chen, Y . Huang, and I. Cohen, “Pearson correlation coefficient,” Noise reduction in speech processing, pp. 1–4, 2009

2009
[53]

Sdr– half-baked or well done?

J. Le Roux, S. Wisdom, H. Erdogan, and J. R. Hershey, “Sdr– half-baked or well done?” in IEEE International Confer- ence on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2019, pp. 626–630

2019
[54]

Learning spectral mapping for speech derever- beration and denoising,

K. Han, Y . Wang, D. Wang, W. S. Woods, I. Merks, and T. Zhang, “Learning spectral mapping for speech derever- beration and denoising,” IEEE/ACM Transactions on Audio, Speech, and Language Processing , vol. 23, no. 6, pp. 982– 992, 2015

2015
[55]

A convolutional recurrent neural net- work for real-time speech enhancement

K. Tan and D. Wang, “A convolutional recurrent neural net- work for real-time speech enhancement.” in Interspeech, vol. 2018, 2018, pp. 3229–3233

2018
[56]

Fullsubnet: A full- band and sub-band fusion model for real-time single-channel speech enhancement,

X. Hao, X. Su, R. Horaud, and X. Li, “Fullsubnet: A full- band and sub-band fusion model for real-time single-channel speech enhancement,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021, pp. 6633–6637

2021
[57]

SEGAN: Speech enhancement generative adversarial network,

S. Pascual, A. Bonafonte, and J. Serra, “SEGAN: Speech enhancement generative adversarial network,” arXiv preprint arXiv:1703.09452, 2017

2017 arXiv
[58]

A new framework for CNN-based speech enhancement in the time domain,

A. Pandey and D. Wang, “A new framework for CNN-based speech enhancement in the time domain,” IEEE/ACM Trans- actions on Audio, Speech, and Language Processing, vol. 27, no. 7, pp. 1179–1188, 2019. 58

2019
[59]

V oicefilter: Targeted voice separation by speaker-conditioned spectrogram masking,

Q. Wang, H. Muckenhirn, K. Wilson, P. Sridhar, Z. Wu, J. Hershey, R. A. Saurous, R. J. Weiss, Y . Jia, and I. L. Moreno, “V oicefilter: Targeted voice separation by speaker-conditioned spectrogram masking,” arXiv preprint arXiv:1810.04826, 2018

2018 arXiv
[60]

Single channel target speaker extraction and recognition with speaker beam,

M. Delcroix, K. Zmolikova, K. Kinoshita, A. Ogawa, and T. Nakatani, “Single channel target speaker extraction and recognition with speaker beam,” in IEEE international con- ference on acoustics, speech and signal processing (ICASSP). IEEE, 2018, pp. 5554–5558

2018
[61]

Per- mutation invariant training of deep models for speaker- independent multi-talker speech separation,

D. Yu, M. Kolbæk, Z.-H. Tan, and J. Jensen, “Per- mutation invariant training of deep models for speaker- independent multi-talker speech separation,” in IEEE Inter- national Conference on Acoustics, Speech and Signal Pro- cessing (ICASSP). IEEE, 2017, pp. 241–245

2017
[62]

Multi-channel overlapped speech recognition with location guided speech extraction network,

Z. Chen, X. Xiao, T. Yoshioka, H. Erdogan, J. Li, and Y . Gong, “Multi-channel overlapped speech recognition with location guided speech extraction network,” in IEEE Spoken Language Technology Workshop (SLT) . IEEE, 2018, pp. 558–565

2018
[63]

Deep clustering: Discriminative embeddings for segmentation and separation,

J. R. Hershey, Z. Chen, J. Le Roux, and S. Watanabe, “Deep clustering: Discriminative embeddings for segmentation and separation,” in IEEE international conference on acoustics, speech and signal processing (ICASSP) . IEEE, 2016, pp. 31–35

2016
[64]

Combining spectral and spatial features for deep learning based blind speaker separation,

Z.-Q. Wang and D. Wang, “Combining spectral and spatial features for deep learning based blind speaker separation,” IEEE/ACM Transactions on audio, speech, and language pro- cessing, vol. 27, no. 2, pp. 457–468, 2018

2018
[65]

Wavesplit: End-to-end speech separation by speaker clustering,

N. Zeghidour and D. Grangier, “Wavesplit: End-to-end speech separation by speaker clustering,” IEEE/ACM Trans- actions on Audio, Speech, and Language Processing, vol. 29, pp. 2840–2849, 2021

2021
[66]

Real-time target sound extraction,

B. Veluri, J. Chan, M. Itani, T. Chen, T. Yoshioka, and S. Gol- lakota, “Real-time target sound extraction,” in IEEE Interna- tional Conference on Acoustics, Speech and Signal Process- ing (ICASSP). IEEE, 2023, pp. 1–5

2023
[67]

Generative adversarial nets,

I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde- Farley, S. Ozair, A. Courville, and Y . Bengio, “Generative adversarial nets,”Advances in Neural Information Processing Systems, vol. 27, 2014

2014
[68]

Conditional generative adversar- ial nets,

M. Mirza and S. Osindero, “Conditional generative adversar- ial nets,” arXiv preprint arXiv:1411.1784, 2014

2014 arXiv
[69]

Unpaired image- to-image translation using cycle-consistent adversarial net- works,

J.-Y . Zhu, T. Park, P. Isola, and A. A. Efros, “Unpaired image- to-image translation using cycle-consistent adversarial net- works,” in Proceedings of the IEEE international conference on computer vision, 2017, pp. 2223–2232

2017
[70]

Semi-supervised learning with generative adver- sarial networks,

A. Odena, “Semi-supervised learning with generative adver- sarial networks,” arXiv preprint arXiv:1606.01583, 2016

2016 arXiv
[71]

Villani et al., Optimal transport: old and new

C. Villani et al., Optimal transport: old and new . Berlin: Springer, 2009, vol. 338

2009
[72]

A geometric view of optimal transportation and generative model,

N. Lei, K. Su, L. Cui, S.-T. Yau, and X. D. Gu, “A geometric view of optimal transportation and generative model,” Com- puter Aided Geometric Design, vol. 68, pp. 1–21, 2019

2019
[73]

The earth mover’s distance as a metric for image retrieval,

Y . Rubner, C. Tomasi, and L. J. Guibas, “The earth mover’s distance as a metric for image retrieval,”International journal of computer vision, vol. 40, pp. 99–121, 2000

2000
[74]

Zhou, Machine learning

Z.-H. Zhou, Machine learning. Springer nature, 2021

2021
[75]

Signal detection theory: Valuable tools for evaluating inductive learning,

K. A. Spackman, “Signal detection theory: Valuable tools for evaluating inductive learning,” inProc. 6th Int. Workshop Mach. Learn. Elsevier, 1989, pp. 160–163

1989
[76]

Understanding diffusion models: A unified perspec- tive,

C. Luo, “Understanding diffusion models: A unified perspec- tive,” arXiv preprint arXiv:2208.11970, 2022

2022 arXiv
[77]

Multidimensional scaling: I. Theory and method,

W. S. Torgerson, “Multidimensional scaling: I. Theory and method,” Psychometrika, vol. 17, no. 4, pp. 401–419, 1952

1952
[78]

Stochastic neighbor embed- ding,

G. E. Hinton and S. Roweis, “Stochastic neighbor embed- ding,” Advances in Neural Information Processing Systems , vol. 15, 2002

2002
[79]

Visualizing data using t- SNE,

L. Van der Maaten and G. Hinton, “Visualizing data using t- SNE,” Journal of Machine Learning Research, vol. 9, no. 11, 2008

2008
[80]

Nonlinear dimensionality re- duction by locally linear embedding,

S. T. Roweis and L. K. Saul, “Nonlinear dimensionality re- duction by locally linear embedding,” Science, vol. 290, no. 5500, pp. 2323–2326, 2000. 59

2000

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.

[1] [1]

Yu and L

D. Yu and L. Deng, Automatic speech recognition. Springer, 2016, vol. 1

work page 2016

[2] [2]

Ro- bust stochastic approximation approach to stochastic pro- gramming,

A. Nemirovski, A. Juditsky, G. Lan, and A. Shapiro, “Ro- bust stochastic approximation approach to stochastic pro- gramming,” SIAM Journal on optimization, vol. 19, no. 4, pp. 1574–1609, 2009

work page 2009

[3] [3]

ADAM: A method for stochastic optimization,

D. P. Kingma and J. Ba, “ADAM: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980, 2014

arXiv 2014

[4] [4]

Comparison of parametric rep- resentations for monosyllabic word recognition in continu- ously spoken sentences,

S. Davis and P. Mermelstein, “Comparison of parametric rep- resentations for monosyllabic word recognition in continu- ously spoken sentences,” IEEE transactions on acoustics, speech, and signal processing , vol. 28, no. 4, pp. 357–366, 1980

work page 1980

[5] [5]

Efficient backprop,

Y . LeCun, L. Bottou, G. B. Orr, and K.-R. M ¨uller, “Efficient backprop,” inNeural networks: Tricks of the trade. Springer, 2002, pp. 9–50

work page 2002

[6] [6]

Rectified linear units improve restricted boltzmann machines,

V . Nair and G. E. Hinton, “Rectified linear units improve restricted boltzmann machines,” in Proceedings of the 27th international conference on machine learning (ICML-10) , 2010, pp. 807–814

2010

[7] [7]

Rectifier non- linearities improve neural network acoustic models,

A. L. Maas, A. Y . Hannun, A. Y . Ng et al., “Rectifier non- linearities improve neural network acoustic models,” inProc. ICML, vol. 30, no. 1. Atlanta, GA, 2013, p. 3

work page 2013

[8] [8]

Searching for ac- tivation functions,

P. Ramachandran, B. Zoph, and Q. V . Le, “Searching for ac- tivation functions,” arXiv preprint arXiv:1710.05941, 2017. 56

arXiv 2017