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REVIEW 3 major objections 8 minor 49 references

Infant Cry Detection In Noisy Environment Using Blueprint Separable Convolutions and Time-Frequency Recurrent Neural Network

T0 review · 3 major / 8 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read A lightweight cry detector built from blueprint separable convolutions and a time-frequency recurrent module stays accurate down to −20 dB SNR, beating much larger baselines.

desk verdict A solid engineering combination with plausible gains, but the split protocol must be clarified before trusting the -20 dB claims. read the letter →

arxiv 2508.19308 v1 pith:P3VITPZN submitted 2025-08-26 cs.SD

Haolin Yu , Yanxiong Li This is my paper
classification cs.SD
keywords InfantcrydetectionBlueprintseparableconvolutionTime-frequencyrecurrentneuralnetworkNoiserobustnessLogMel-spectrogramAttentionmechanismLow-complexityaudioclassificationDataaugmentation
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 sets out to show that infant cry detection can be made both lightweight and noise-robust enough for home monitoring. It builds a detector from blueprint separable convolutions—which factor each standard filter into a pointwise weight shared across channels and a 2D blueprint—plus a time-frequency recurrent denoising stage and two attention mechanisms. Trained on a multi-source public corpus corrupted with household noise, the model reports 91.3% accuracy and 90.8% F1-score at −20 dB signal-to-noise ratio, above ResNet18, MobileNetV2, ECAPA-TDNN, and BiLSTM, with 1.54 million parameters and 0.46 GFLOPs. The practical payoff, if correct, is a cry detector that fits memory-limited baby-care devices without giving up accuracy in noisy rooms.

What carries the argument

The load-bearing identity is the Blueprint Separable Convolution decomposition, which rewrites the nth filter's c-th channel as ω_c^(n)·B^(n), a per-channel scalar times a shared 2D blueprint. That cuts the parameter count from C·K²·N to (C+K²)·N while keeping most of a regular convolution's expressiveness. The other mechanism is the Adaptive Denoising Module: a BiLSTM processes each STFT frame to capture harmonic structure, an LSTM then tracks temporal dependencies per frequency bin, and residual connections stabilize training; together with contrast-aware channel attention (mean-plus-standard-deviation pooling) and enhanced spatial attention, the model is meant to suppress noise rather tha

What would settle it

Run a speaker-disjoint evaluation on CryCeleb2023, ensuring no vocalization of any infant in the training set appears in the test set; if the F1-score at −20 dB falls to the level of ResNet18's, the robustness claim fails. A complementary check is deploying the model on continuous, unsegmented home audio and measuring false-alarm rates per hour.

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Extended reading notes

Core claim

On its own terms, the paper's central claim is that replacing standard convolutions with blueprint separable convolutions, then feeding multi-scale embeddings through a BiLSTM/LSTM adaptive denoising module, gives a cry/non-cry classifier that degrades gracefully as noise rises. When trained on clean audio and tested on 0 dB mixtures, the proposed model loses only 19.0 accuracy points versus 24.3–31.1 for the baselines, keeping F1-score at 74.3% where the next best is 63.9%. When trained on noisy audio covering 0 to −20 dB SNR, the model holds 91.3% accuracy and 90.8% F1-score at the hardest condition, against ResNet18's 84.8% and 81.3%. Its parameter count (1.54M) and FLOPs (0.46G) are smal

Load-bearing premise

The reported results rest on five-fold cross-validation over randomly split 5-second clips from public datasets; if clips from the same infant land in both training and test folds, the model could be recalling infant-specific voice traits rather than learning general cry patterns, inflating the numbers relative to unseen-baby deployments.

Editorial extensions

If this is right

  • A detector with 1.54M parameters and 0.46 GFLOPs fits the memory and power budgets of typical infant-monitor hardware, making real-time on-device cry detection plausible.
  • Training on noisy, multi-source data is not optional: the paper's clean-trained model loses 25.5 F1 points on 0 dB noise, while the noisy-trained model stays near 98% F1 at 0 dB.
  • The robustness gain concentrates in the recurrent denoising stage; removing ADM costs about 3.7 F1 points at 0 dB, more than removing ESA, CCA, or multi-scale concatenation.
  • At −20 dB, the method keeps recall at 91.2%, while every compared baseline falls below 71%; a detector that works at such SNR could catch cries buried under loud household sounds.

Reading between the lines

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

  • The five-fold split described in the paper is clip-level, so the same infant's vocalizations can appear in both training and test folds; a speaker-disjoint split would test whether the model learned cry-general features rather than speaker identity.
  • Because the architecture is generic (BSConv encoder plus a time-frequency recurrent module), the same recipe should transfer to other short acoustic events such as coughs, screams, or pet vocalizations; this extension is not tested in the paper.
  • The −20 dB condition implies strong denoising; comparing the method against a standard speech-enhancement front end feeding the same classifier would isolate what the ADM contributes.
  • The paper suggests an energy-threshold sliding-window deployment but does not evaluate it; measuring false-alarm rates on continuous home recordings would confirm the practical claim.
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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 / 8 minor

Summary. The paper proposes a lightweight infant cry detection method for noisy environments. The model combines blueprint separable convolutions (BSConv) with multi-scale feature extraction, enhanced spatial attention (ESA), contrast-aware channel attention (CCA), and a time-frequency recurrent module (BiLSTM/LSTM) that the authors call an Adaptive Denoising Module (ADM). Inputs are log Mel-spectrograms extracted from 5-second audio clips. The authors integrate several public datasets (CryCeleb2023, EnesBabyCries1, iFLYTEK, plus non-cry sources) and apply augmentations including speed perturbation, additive noise at SNRs from 0 to -20 dB, reverberation, and time/frequency masking. Experiments compare the method with BiLSTM, ECAPA-TDNN, MobileNetV2, and ResNet18 under clean and noisy conditions, report an ablation study, and give model complexity in parameters and FLOPs. The central empirical claim is that the proposed method achieves 91.3% accuracy and 90.8% F1-score at -20 dB with 1.54M parameters and 0.46G FLOPs, outperforming the compared backbones.

Significance. If the reported results are valid, the paper makes a useful contribution to edge-deployable infant cry detection: the model is small (1.54M parameters), computationally light (0.46G FLOPs), and shows markedly better robustness at -20 dB than the generic backbones. The code is released, the dataset integration across multiple public corpora is a strength, and the T-F recurrent design for noise robustness is a reasonable architectural idea. The ablation study indicates that each component contributes. However, the empirical claims rest entirely on the evaluation protocol, and the protocol has a potentially serious subject-leakage problem that must be resolved before the results can be trusted.

major comments (3)
  1. [Sec. IV.A and IV.C, Table III] The five-fold cross-validation may suffer from subject/recording leakage. CryCeleb2023 is explicitly a speaker-verification dataset with multiple cry segments per infant, and EnesBabyCries1 also contains repeated recordings per infant. The paper states only that 'five-fold cross-validation is performed with the average results reported' and does not describe a subject-disjoint or recording-disjoint split. If the folds are random at the 5-second clip level, the same infant or contiguous segments from the same original recording can appear in both training and test folds, allowing the model to memorize speaker-specific timbre, pitch, and recording artifacts. This would inflate the reported accuracy, especially at -20 dB (Table III), and undermine the paper's main generalization claim. Please clarify the exact splitting procedure and, ideally, rerun the experiments with a subject-disjoint s
  2. [Sec. IV.C and Tables II-IV] No variance or confidence intervals are reported. All results are point estimates averaged over five folds, and no standard deviation, per-fold results, or statistical significance tests are given. While the -20 dB margin over ResNet18 (91.3 vs. 84.8 accuracy) is large, some comparisons at 0 dB and in the ablation are close, so without error bars it is difficult to assess whether the claimed improvements are reliable. Please report per-fold results or standard deviations, and where possible a paired significance test.
  3. [Sec. IV.C and Table III] The comparison set is limited to generic audio/speaker backbones (BiLSTM, ECAPA-TDNN, MobileNetV2, ResNet18). No specialized infant cry detectors from the cited related work (e.g., Refs. [1], [2], [6], [31]) are included, so the statement that the method 'exceeds many state-of-the-art methods' is only supported against four generic models. Adding at least one or two cry-specific baselines, or qualifying the claim, would make the evaluation more convincing.
minor comments (8)
  1. [Table II caption] Typo: 'OBATINED' should be 'OBTAINED'.
  2. [Eq. (4)] The subscript '𝒸' is nonstandard and may render incorrectly; use a plain 'c' consistently. Also, the square-root expression should be checked for clarity: the formula as written uses 'm' both inside and outside the sum, and the reader must infer the intended standard deviation plus mean.
  3. [Sec. III.A.2 / Fig. 1] The text says 'convolution groups, implemented by BSConv as well' but the caption of Fig. 1 is not visible in the text; ensure the figure clearly labels BSConv, max-pooling, and bilinear upsampling.
  4. [Sec. IV.A] The phrase 'with 5 seconds in length' is awkward; use 'of 5 seconds' or '5-second recordings'.
  5. [References [25]] There is a typo in the author list: 'Levr ro' should likely be 'Levrero'.
  6. [Sec. IV.B] The speed perturbation description says 'uniformly selects a speed-up factor from 0.8 to 1.2 relative to the original sampling rate' but does not state whether the resampled audio is time-stretched without pitch change or with pitch change. This affects the interpretation of the augmentation.
  7. [Sec. V / Abstract] The phrase 'exceeds many state-of-the-art methods' is vague; specify the actual comparison set in the abstract or conclusions.
  8. [Sec. III.B] The ADM is called 'Adaptive Denoising Module' but the model is trained only with a classification loss; no denoising reconstruction objective is used. The term 'denoising' is therefore used metaphorically. Consider clarifying that the module learns noise-invariant representations rather than explicitly reconstructing a clean signal.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation chain: empirical test-set results with non-load-bearing background self-citations.

full rationale

The paper's central claims are empirical: Table III reports accuracy/F1 on noisy test sets after training, and Table V reports parameter/FLOP counts. These are measured outcomes, not quantities derived from the model's definition. The proposed architecture (BSConv encoder, T-F RNN, attention modules) is a design choice, not a fitted function whose outputs are then relabeled as predictions. The only self-citations are [19] (prior use of BSConv by one author) and [33]-[42] (prior audio work using log Mel-spectrograms); neither is load-bearing, since BSConv is sourced to external work [18] and the use of log Mel-spectrograms is standard independent practice. The possible subject/recording overlap in the five-fold split is an evaluation-validity concern, not a circularity concern: even if leakage exists, it would inflate results empirically, not make them equal to the inputs by construction. No equation reduces a predicted quantity to a fitted parameter, and no load-bearing argument rests on an author-imposed uniqueness claim. Therefore no significant circularity is present.

Assumptions & free parameters 7 free parameters · 4 assumptions · 0 invented entities

No new physical entities or external quantities are postulated; the model is a composition of published modules. The main unexamined commitments are dataset and split assumptions, plus hand-picked hyperparameters.

free parameters (7)
  • Input segment length = 5 seconds
    Chosen to capture one or more cry cycles; no sensitivity analysis; affects class balance and compute.
  • Mel filterbank size = 128
    Standard choice; no ablation on bin count.
  • STFT window/hop = 512/400 samples at 16 kHz
    Chosen for spectrogram resolution; not varied.
  • SNR augmentation range = 0 to -20 dB
    Chosen to simulate foreground/background cry scenarios; no analysis of distributional match to real homes.
  • Number of mixed noise sources = 2 to 3
    Hand-picked to simulate multi-source interference; no sensitivity analysis.
  • Speed perturbation range = 0.8x to 1.2x
    Uniform random factor from prior speech augmentation practice.
  • Architecture hyperparameters = not reported
    Hidden sizes and layer counts of BiLSTM/LSTM and BSConv blocks are omitted, so effective capacity is a free design choice.
assumptions (4)
  • domain assumption Infant cries are quasi-stationary within short windows and have harmonic spectral structure, while household noise is more transient
    Used to justify the time-frequency RNN denoising module (Sec. III.B.2); if false, the ADM's design premise collapses.
  • domain assumption Sampling at 16 kHz captures relevant cry information because the typical high vocal tract formant is below 6 kHz
    Adopted from ref. [32] in Sec. IV.A; controls all input representations.
  • domain assumption The merged public datasets are diverse and representative of real-world cry and non-cry audio
    Central to external validity; the paper provides no field validation or demographic match to deployment homes (Sec. IV.A).
  • domain assumption Five-fold cross-validation partitions are statistically independent samples
    Sec. IV.C reports only 'five-fold cross-validation' without speaker-disjoint partitioning; if same infants appear across folds, test results are optimistically biased.

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Cite this review

Pith. "Pith review of Infant Cry Detection In Noisy Environment Using Blueprint Separable Convolutions and Time-Frequency Recurrent Neural Network." pith.science (2026). https://pith.science/paper/P3VITPZN

@misc{pith2026250819308,
  author       = {Pith},
  title        = {Pith review of: Infant Cry Detection In Noisy Environment Using Blueprint Separable Convolutions and Time-Frequency Recurrent Neural Network},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/P3VITPZN}},
  note         = {Machine review of arXiv:2508.19308}
}
read the original abstract

Infant cry detection is a crucial component of baby care system. In this paper, we propose a lightweight and robust method for infant cry detection. The method leverages blueprint separable convolutions to reduce computational complexity, and a time-frequency recurrent neural network for adaptive denoising. The overall framework of the method is structured as a multi-scale convolutional recurrent neural network, which is enhanced by efficient spatial attention mechanism and contrast-aware channel attention module, and acquire local and global information from the input feature of log Mel-spectrogram. Multiple public datasets are adopted to create a diverse and representative dataset, and environmental corruption techniques are used to generate the noisy samples encountered in real-world scenarios. Results show that our method exceeds many state-of-the-art methods in accuracy, F1-score, and complexity under various signal-to-noise ratio conditions. The code is at https://github.com/fhfjsd1/ICD_MMSP.

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Works this paper leans on

49 extracted references · 47 canonical work pages

  1. [1]

    Spec - trogram and lstm based infant cry detection method for infant wellness monitoring systems,

    S. P. Narayanan, M. S. Manikandan, and L. R. Cenkeramaddi, “Spec - trogram and lstm based infant cry detection method for infant wellness monitoring systems,” in 2024 16th International Conference on Human System Interaction (HSI). IEEE, 2024, pp. 1-4

    work page 2024

  2. [2]

    Multi-task learning for audio-based infant cry detection and reasoning,

    M. Xia, D. Huang, and W. Wang, “Multi-task learning for audio-based infant cry detection and reasoning,” IEEE Journal of Biomedical and Health Informatics, 2024

    work page 2024

  3. [6]

    Infant cry detection using causal temporal representation,

    M. Fu, D. Li, A. Gadhiya, B. Lambright, M. Alowais, M. Bahnassy, S. E. D. Elletter, H. O. Toyin, H. Jiang, K. Zhang et al., “Infant cry detection using causal temporal representation,” in 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025, pp. 1-5

    work page 2025

  4. [31]

    Investigation of infants’ crying detection in noisy home scene with deep learning,

    J. Lin, B. Cheng, and J. Lei, “Investigation of infants’ crying detection in noisy home scene with deep learning,” in 2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall). IEEE, 2022, pp. 1-6

    work page 2022

  5. [3]

    Fast autocorrelation feature-based infant cry detector for resource-efficient affordable edge cry sound analysis systems,

    S. P. Narayanan, M. S. Manikandan, and L. R. Cenkeramaddi, “Fast autocorrelation feature-based infant cry detector for resource-efficient affordable edge cry sound analysis systems,” in 2024 IEEE 19th Con- ference on Industrial Electronics and Applications (ICIEA). IEEE, 2024, pp. 1-6

    work page 2024

  6. [4]

    Infant crying detection in real -world environments,

    X. Yao, M. Micheletti, M. Johnson, E. Thomaz, and K. de Barbaro, “Infant crying detection in real -world environments,” in 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022, pp. 131–135

    work page 2022

  7. [5]

    Real-time baby crying detection in the noisy everyday environment,

    L. S. Foo, W.-S. Yap, Y. C. Hum, Z. Kadim, H. W. Hon, and Y. K. Tee, “Real-time baby crying detection in the noisy everyday environment,” in 2020 11th IEEE Control and System Graduate Research Colloquium (ICSGRC). IEEE, 2020, pp. 26–31

    work page 2020

  8. [7]

    Infant speech emotion recog - nition based on channel attention mechanism with resnet-bilstm,

    X. Shen, B. Lv, T. Liu, and Q. Cheng, “Infant speech emotion recog - nition based on channel attention mechanism with resnet-bilstm,” in 2024 5th International Conference on Information Science, Parallel and Distributed Systems (ISPDS). IEEE, 2024, pp. 54-57

    work page 2024

Show all 49 references
  1. [8]

    Baby cry recognition based on slgan model data generation and deep feature fusion,

    K. Zhang, H.-N. Ting, and Y.-M. Choo, “Baby cry recognition based on slgan model data generation and deep feature fusion,” Expert Systems with Applications, vol. 242, p. 122681, 2024

    2024

  2. [9]

    A multistage heterogeneous stacking ensemble model for augmented infant cry classification,

    V. R. Joshi, K. Srinivasan, P. D. R. Vincent, V. Rajinikanth, and C.- Y. Chang, “A multistage heterogeneous stacking ensemble model for augmented infant cry classification,” Frontiers in Public Health , vol. 10, p. 819865, 2022

    2022

  3. [10]

    Infant cry language analysis and recognition: an experimental approach,

    L. Liu, W. Li, X. Wu, and B. X. Zhou, “Infant cry language analysis and recognition: an experimental approach,” IEEE/CAA Journal of Automatica Sinica, vol. 6, no. 3, pp. 778-788, 2019

    2019

  4. [11]

    Infant cry classification using transfer learning,

    G. Anjali, S. Sanjeev, A. Mounika, G. Suhas, G. P. Reddy, and Y. Kshi- raja, “Infant cry classification using transfer learning,” in TENCON 2022-2022 IEEE Region 10 Conference (TENCON) . IEEE, 2022, pp. 1-7

    2022

  5. [12]

    A multi -scale convolutional at- tention neural network based on residual block downsampling for infant cry classification and detection,

    J. Yang, Z. Zhang, J. Li, and C. Lin, “A multi -scale convolutional at- tention neural network based on residual block downsampling for infant cry classification and detection,” in 2024 6th International Conference on Internet of Things, Automation and Artificial Intelligence ...

    2024

  6. [13]

    A machine learning approach to classify biomedical acoustic features for baby cries,

    G. Aggarwal, K. Jhajharia, J. Izhar, M. Kumar, and L. Abualigah, “A machine learning approach to classify biomedical acoustic features for baby cries,” Journal of Voice, 2023

    2023

  7. [14]

    A systematic literature review of audio signal processing methods for infant cry recognition and interpretation,

    N. G. Setyoningrum, E. Utami, F. W. Wibowo et al., “A systematic literature review of audio signal processing methods for infant cry recognition and interpretation,” in 2024 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM). IEEE, 20...

    2024

  8. [15]

    Exploring the differences between deaf and hearing infant cries,

    E. Hoq and I. Nwogu, “Exploring the differences between deaf and hearing infant cries,” in ICASSP 2025 -2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2025, pp. 1-5

    2025

  9. [16]

    Self-supervised learning for infant cry analysis,

    A. Gorin, C. Subakan, S. Abdoli, J. Wang, S. Latremouille, and C. Onu, “Self-supervised learning for infant cry analysis,” in 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW). IEEE, 2023, pp. 1-5

    2023

  10. [17]

    A system for the processing of infant cry to recognize pathologies in recently born babies with neural networks,

    O. F. Reyes -Galaviz and C. A. Reyes -Garcia, “A system for the processing of infant cry to recognize pathologies in recently born babies with neural networks,” in 9th Conference Speech and Computer (SPECOM). Citeseer, 2004, pp. 1-6

    2004

  11. [18]

    Rethinking depthwise separable convo- lutions: How intra-kernel correlations lead to improved mobilenets,

    D. Haase and M. Amthor, “Rethinking depthwise separable convo- lutions: How intra-kernel correlations lead to improved mobilenets,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 14 600-14 609

    2020

  12. [19]

    Low-complexity acoustic scene classification using blueprint separable convolution and knowledge distillation ,

    J. Tan and Y. Li, “Low-complexity acoustic scene classification using blueprint separable convolution and knowledge distillation ,” in Technical Report of IEEE DCASE, 2023, pp. 1-4

    2023

  13. [20]

    Residual feature aggregation network for image super-resolution,

    J. Liu, W. Zhang, Y. Tang, J. Tang, and G. Wu, “Residual feature aggregation network for image super-resolution,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 2359-2368

    2020

  14. [21]

    Phase- aware speech enhancement with deep complex u-net,

    H.-S. Choi, J.-H. Kim, J. Huh, A. Kim, J.-W. Ha, and K. Lee, “Phase- aware speech enhancement with deep complex u-net,” in International Conference on Learning Representations, 2018

    2018

  15. [22]

    Dccrn: Deep complex convolution recurrent network for phase-aware speech enhancement,

    Y. Hu, Y. Liu, S. Lv, M. Xing, S. Zhang, Y. Fu, J. Wu, B. Zhang, and L. Xie, “Dccrn: Deep complex convolution recurrent network for phase-aware speech enhancement,” arXiv preprint arXiv:2008.00264, 2020

    2008 arXiv

  16. [23]

    Dual-path rnn: efficient long sequence modeling for time-domain single-channel speech separation,

    Y. Luo, Z. Chen, and T. Yoshioka, “Dual-path rnn: efficient long sequence modeling for time-domain single-channel speech separation,” in ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020, pp. 46-50

    2020

  17. [24]

    Cryceleb: a speaker verification dataset based on infant cry sounds,

    D. Budaghyan, C. C. Onu, A. Gorin, C. Subakan, and D. Precup, “Cryceleb: a speaker verification dataset based on infant cry sounds,” in ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2024, pp. 11 966 –11 970

    2024

  18. [25]

    Infant cries convey both stable and dynamic information about age and identity,

    M. Lockhart-Bouron, A. Anikin, K. Pisanski, S. Corvin, C. Cornec, L. Papet, F. Levr ro, C. Fauchon, H. Patural, D. Reby et al., “Infant cries convey both stable and dynamic information about age and identity,” Communications Psychology, vol. 1, no. 1, p. 26, 2023

    2023

  19. [26]

    iFLYTEK A.I. Developer Competition - Baby Cry Recognition Challenge,

    “iFLYTEK A.I. Developer Competition - Baby Cry Recognition Challenge,” Aug. 2020 [Online]. Available: http://challenge.xfyun.cn/topic/info?type=baby-crying

    2020

  20. [27]

    The vox celeb speaker recognition challenge: A retrospective,

    J. Huh, J. S. Chung, A. Nagrani, A. Brown, J.-w. Jung, D. Garcia- Romero, and A. Zisserman, “The vox celeb speaker recognition challenge: A retrospective,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2024

    2024

  21. [28]

    Esc: Dataset for environmental sound classification,

    K. J. Piczak, “Esc: Dataset for environmental sound classification,” in Proceedings of the 23rd ACM international conference on Multimedia, 2015, pp. 1015-1018

    2015

  22. [29]

    Catmeows: A publicly-available dataset of cat vocaliza- tions,

    L. A. Ludovico, S. Ntalampiras, G. Presti, S. Cannas, M. Battini, and S. Mattiello, “Catmeows: A publicly-available dataset of cat vocaliza- tions,” in International Conference on Multimedia Modeling. Springer, 2021, pp. 230-243

    2021

  23. [30]

    Dasee a synthetic database of domestic acoustic scenes and events in dementia patients environment,

    A. Copiaco, C. Ritz, S. Fasciani, and N. Abdulaziz, “Dasee a synthetic database of domestic acoustic scenes and events in dementia patients environment,” arXiv preprint arXiv:2104.13423, 2021

    2021 arXiv

  24. [32]

    Convolutional neural networks for audio-based continuous infant cry monitoring at home,

    J. Xie, X. Long, R. A. Otte, and C. Shan, “Convolutional neural networks for audio-based continuous infant cry monitoring at home,” IEEE Sensors Journal, vol. 21, no. 24, pp. 27 710-27 717, 2021

    2021

  25. [33]

    Mobile phone clustering from speech recordings using deep representation and spectral clustering,

    Y. Li, X. Zhang, X. Li, Y. Zhang, J. Yang and Q. He, "Mobile phone clustering from speech recordings using deep representation and spectral clustering," in IEEE Transactions on Information Forensics and Security, vol. 13, no. 4, pp. 965-977, April 2018

    2018

  26. [34]

    Domestic activities clustering from audio recordings using convolutional capsule autoencoder network,

    Z. Lin et al ., "Domestic activities clustering from audio recordings using convolutional capsule autoencoder network," IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2021, pp. 835-839

    2021

  27. [35]

    Speaker clustering by co-optimizing deep representation learning and cluster estimation,

    Y. Li, W. Wang, M. Liu, Z. Jiang and Q. He, "Speaker clustering by co-optimizing deep representation learning and cluster estimation," in IEEE Transactions on Multimedia, vol. 23, pp. 3377-3387, 2021

    2021

  28. [36]

    Acoustic scene clustering using joint optimization of deep embedding learning and clustering iteration,

    Y. Li, M. Liu, W. Wang, Y. Zhang and Q. He, "Acoustic scene clustering using joint optimization of deep embedding learning and clustering iteration," in IEEE Transactions on Multimedia, vol. 22, no. 6, pp. 1385-1394, June 2020

    2020

  29. [37]

    Few -shot speaker identification using lightweight prototypical network with feature grouping and interaction,

    Y. Li, H. Chen, W. Cao, Q. Huang and Q. He, "Few -shot speaker identification using lightweight prototypical network with feature grouping and interaction," in IEEE Transactions on Multimedia , vol. 25, pp. 9241-9253, 2023

    2023

  30. [38]

    Lightweight speaker verification using transformation module with feature partition and fusion,

    Y. Li, Z. Jiang, Q. Huang, W. Cao and J. Li, "Lightweight speaker verification using transformation module with feature partition and fusion," in IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 32, pp. 794-806, 2024

    2024

  31. [39]

    Few-shot class-incremental audio classification with adaptive mitigation of forgetting and overfitting,

    Y. Li, J. Li, Y. Si, J. Tan and Q. He, "Few-shot class-incremental audio classification with adaptive mitigation of forgetting and overfitting," in IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 32, pp. 2297-2311, 2024

    2024

  32. [40]

    Fully few -shot class-incremental audio classification with adaptive improvement of stability and plasticity,

    Y. Si, Y. Li, J. Tan, G. Chen, Q. Li and M. Russo, "Fully few -shot class-incremental audio classification with adaptive improvement of stability and plasticity," in IEEE Transactions on Audio, Speech and Language Processing, vol. 33, pp. 418-433, 2025

    2025

  33. [41]

    Few -shot class - incremental audio classification using dynamically expanded classifier with self -attention modified prototypes,

    Y. Li, W. Cao, W. Xie, J. Li and E. Benetos, "Few -shot class - incremental audio classification using dynamically expanded classifier with self -attention modified prototypes," in IEEE Transactions on Multimedia, vol. 26, pp. 1346-1360, 2024

    2024

  34. [42]

    Acoustic scene classification using deep audio feature and BLSTM network,

    Y. Li, X. Li, Y. Zhang, W. Wang, M. Liu and X. Feng, "Acoustic scene classification using deep audio feature and BLSTM network," International Conference on Audio, Language and Image Processing (ICALIP), 2018, pp. 371-374

    2018

  35. [43]

    Audio augmentation for speech recognition

    T. Ko, V. Peddinti, D. Povey, and S. Khudanpur, “Audio augmentation for speech recognition.” in Interspeech, vol. 2015, 2015, p. 3586

    2015

  36. [44]

    A study on data augmentation of reverberant speech for robust speech recognition,

    T. Ko, V. Peddinti, D. Povey, M. L. Seltzer, and S. Khudanpur, “A study on data augmentation of reverberant speech for robust speech recognition,” in 2017 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, 2017, pp. 5220-5224

    2017

  37. [45]

    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 conference on computer vision and pattern recognition, 2016, pp. 770-778

    2016

  38. [46]

    Ecapa -tdnn: Em- phasized channel attention, propagation and aggregation in tdnn based speaker verification,

    B. Desplanques, J. Thienpondt, and K. Demuynck, “Ecapa -tdnn: Em- phasized channel attention, propagation and aggregation in tdnn based speaker verification,” arXiv preprint arXiv:2005.07143, 2020

    2005 arXiv

  39. [47]

    Mobilenetv2: Inverted residuals and linear bottlenecks,

    M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “Mobilenetv2: Inverted residuals and linear bottlenecks,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 4510-4520

    2018

  40. [48]

    A cyclical learning rate method in deep learning training,

    J. Li and X. Yang, “A cyclical learning rate method in deep learning training,” in 2020 International Conference on Computer, Information and Telecommunication Systems (CITS). IEEE, 2020, pp. 1-5

    2020

  41. [49]

    Robust laughter detection in noisy environments

    J. Gillick, W. Deng, K. Ryokai, and D. Bamman, “Robust laughter detection in noisy environments.” in Interspeech, vol. 2021, 2021, pp. 2481-2485

    2021

Pith tools

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