Privacy-Enhancing Infant Cry Classification with Federated Transformers and Denoising Regularization

• URL: http://arxiv.org/abs/2512.13880v1
• Date: Mon, 15 Dec 2025 20:33:24 GMT
• Title: Privacy-Enhancing Infant Cry Classification with Federated Transformers and Denoising Regularization
• Abstract summary: We present an end-to-end infant cry analysis pipeline that integrates a denoising autoencoder, a convolutional tokenizer, and a Transformer encoder.<n>The system performs on-device denoising, adaptive segmentation, post hoc calibration, and energy-based out-of-distribution (OOD) abstention.<n>Using the Baby Chillanto and Donate-a-Cry datasets, the model achieves a macro F1 score of 0.938, an AUC with 0.962, and an Expected Error (ECE) of 0.032, while reducing per-round client upload from approximately 36 to 42 MB to 3.3 MB
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Infant cry classification can aid early assessment of infant needs. However, deployment of such solutions is limited by privacy concerns around audio data, sensitivity to background noise, and domain shift across recording environments. We present an end-to-end infant cry analysis pipeline that integrates a denoising autoencoder (DAE), a convolutional tokenizer, and a Transformer encoder trained using communication-efficient federated learning (FL). The system performs on-device denoising, adaptive segmentation, post hoc calibration, and energy-based out-of-distribution (OOD) abstention. Federated training employs a regularized control variate update with 8-bit adapter deltas under secure aggregation. Using the Baby Chillanto and Donate-a-Cry datasets with ESC-50 noise overlays, the model achieves a macro F1 score of 0.938, an AUC of 0.962, and an Expected Calibration Error (ECE) of 0.032, while reducing per-round client upload from approximately 36 to 42 MB to 3.3 MB. Real-time edge inference on an NVIDIA Jetson Nano (4 GB, TensorRT FP16) achieves 96 ms per one-second spectrogram frame. These results demonstrate a practical path toward privacy-preserving, noise-robust, and communication-efficient infant cry classification suitable for federated deployment.

Related papers

• An Adaptive Gradient Clipping and Noise Injection Mechanism for Differentially Private Federated Learning [1.5876034680722233]
  This paper presents DDP-SA-adaptive, an adaptive gradient clipping and noise adding mechanism for differentially private federated learning.<n>Compared with the static DDP-SA baseline, DDP-SA-adaptive reduces the number of communication rounds by 6.81%, total training time by 19.21%, and average per-round training time by 13.33%.<n>It also reduces test loss by 98.74% and increases test R2 by 3.41%, leading to improved model accuracy.
  arXiv  Detail & Related papers  (2026-08-15T10:10:34Z)
• Prototype-Rectified Iterative Self-supervised Manifold Denoising under Severe Acoustic Shift [3.704942101286518]
  We introduce PRISM, a training-free, source-free Test-Time Adaptation (TTA) framework grounded in the Affine Noise Hypothesis.<n>At inference, adaptation reduces to one matrix-vector multiplication in 0.0009 ms, making it substantially faster than gradient-based TTA.<n>On UrbanSound8K, PRISM improves over the zero-shot baseline by 12.94 percentage points and surpasses an oracle-assisted TTA baseline by 9.41 percentage points.
  arXiv  Detail & Related papers  (2026-08-15T04:42:14Z)
• Task-Oriented Candidate-Latent Feedback for Coarse-to-Fine Sensing in Distributed OFDM-ISAC Networks [6.604029885021074]
  Future integrated sensing and communication (ISAC) architectures separate the sensing entity (SE) that acquires measurements from the sensing function (SF) that performs inference.<n>We propose a learning-based coarse-to-fine sensing pipeline with candidate-latent feedback for single-target estimation.
  arXiv  Detail & Related papers  (2026-08-04T08:26:06Z)
• Show the Signal, Hide the Noise: Spectral Forcing for Pixel-Space Diffusion [46.51681899437508]
  We introduce Spectral Forcing, a parameter-free, time-conditional 2D-DCT low-pass operator applied to the noisy input before the patch embedder.<n>Its cutoff expands monotonically with the diffusion time and becomes the identity at the data endpoint.<n>On ImageNet-256 with JiT-700M/32, Spectral Forcing consistently improves both FID and Inception Score across different training epochs.
  arXiv  Detail & Related papers  (2026-06-13T10:29:11Z)
• How Much Capacity Does EEG Denoising Need? Ultra-Compact Networks reveal Benchmark Saturation and Metric-Utility Gap [0.0]
  Deep learning EEG denoising architectures have scaled from tens of thousands to tens of millions of parameters, yet no prior study has isolated model capacity as the experimental variable.<n>We address both gaps by fixing architecture, loss, data split, and training recipe while sweeping only channel width from 1.05K to 40.26K parameters in a minimal depthwiseseparable convolutional U-Net.
  arXiv  Detail & Related papers  (2026-06-07T12:17:25Z)
• Noise-Induced Landscape Distortion in QAOA for Constrained Binary Optimization: Empirical Characterization on IBM Quantum Hardware [0.0]
  We introduce and empirically validate Landscape Span Compression (LSC)<n>LSC measures how much noise flattens the energy landscape, approaching 1 as the landscape collapses toward a barren plateau.<n>We report an experience study of applying QAOA with LSC-based noise characterization on IBM's ibm_fez for three constrained QUBO portfolio instances.
  arXiv  Detail & Related papers  (2026-04-21T12:55:50Z)
• WISV: Wireless-Informed Semantic Verification for Distributed Speculative Decoding in Device-Edge LLM Inference [56.297697169678095]
  WISV (Wireless-Informed Semantic Verification) is a novel distributed speculative decoding framework.<n>We show that WISV achieves up to a 60.8% increase in accepted length, a 37.3% reduction in interaction rounds, and a 31.4% improvement in end-to-end latency.<n>We validate WISV on a hardware testbed comprising an NVIDIA Jetson AGX Orin and an A40-equipped server.
  arXiv  Detail & Related papers  (2026-04-20T01:29:56Z)
• D3R-Net: Dual-Domain Denoising Reconstruction Network for Robust Industrial Anomaly Detection [0.0]
  Unsupervised anomaly detection (UAD) is a key ingredient of automated visual inspection in modern manufacturing.<n>We introduce D3R-Net, a Dual-Domain Denoising Reconstruction framework that couples a self-supervised 'healing' task with frequency-aware regularization.<n>In addition to the spatial mean squared error, we employ a Fast Fourier Transform (FFT) magnitude loss that encourages consistency in the frequency domain.
  arXiv  Detail & Related papers  (2026-01-27T23:21:59Z)
• MBFL-DKMR: Improving Mutation-based Fault Localization through Denoising-based Kill Matrix Refinement [21.09532467931481]
  We propose a novel approach to refine the kill matrix, a core data structure capturing mutant-test relationships in MBFL.<n>We introduce DKMR, which employs two key stages: signal enhancement through hybrid matrix construction to improve the signal-to-noise ratio for better denoising, and signal denoising via frequency domain filtering to suppress noise.<n>Our evaluation on Defects4J v2.0.0 demonstrates that MBFL-DKMR effectively mitigates the noise and outperforms the state-of-the-art MBFL techniques.
  arXiv  Detail & Related papers  (2025-11-28T06:48:00Z)
• MeanFlow Transformers with Representation Autoencoders [71.45823902973349]
  MeanFlow (MF) is a diffusion-motivated generative model that enables efficient few-step generation by learning long jumps directly from noise to data.<n>We develop an efficient training and sampling scheme for MF in the latent space of a Representation Autoencoder (RAE)<n>We achieve a 1-step FID of 2.03, outperforming vanilla MF's 3.43, while reducing sampling GFLOPS by 38% and total training cost by 83% on ImageNet 256.
  arXiv  Detail & Related papers  (2025-11-17T06:17:08Z)
• Physics-informed GNN for medium-high voltage AC power flow with edge-aware attention and line search correction operator [7.927606793404479]
  Physics-informed graph neural networks (PIGNNs) have emerged as fast AC power-flow solvers.<n>PIGNN-Attn-LS achieves a test RMSE of 0.00033 p.u. in voltage and 0.08$circ$ in angle, outperforming the PIGNN-MLP baseline by 99.5% and 87.1%, respectively.
  arXiv  Detail & Related papers  (2025-09-26T15:09:26Z)
• TPA: Temporal Prompt Alignment for Fetal Congenital Heart Defect Classification [2.3974223785103166]
  Congenital heart defect (CHD) detection in ultrasound videos is hindered by image noise and probe positioning variability.<n>We propose Temporal Prompt Alignment (TPA), a method leveraging foundation image-text model and prompt-aware contrastive learning.<n>TPA extracts features from each frame of video subclips using an image encoder, aggregates them with a trainable temporal extractor, and aligns the video representation with class-specific text prompts.
  arXiv  Detail & Related papers  (2025-08-21T06:36:47Z)
• Unsupervised CP-UNet Framework for Denoising DAS Data with Decay Noise [13.466125373185399]
  Distributed acoustic sensor (DAS) technology leverages optical fiber cables to detect acoustic signals.<n>DAS exhibits a lower signal-to-noise ratio (S/N) compared to geophones.<n>This reduced S/N can negatively impact data analyses containing inversion and interpretation.
  arXiv  Detail & Related papers  (2025-02-19T03:09:49Z)
• Data-Free Dynamic Compression of CNNs for Tractable Efficiency [46.498278084317704]
  structured pruning approaches have shown promise in lowering floating-point operations without substantial drops in accuracy.<n>We propose HASTE (Hashing for Tractable Efficiency), a data-free, plug-and-play convolution module that instantly reduces a network's test-time inference cost without training or fine-tuning.<n>We demonstrate our approach on the popular vision benchmarks CIFAR-10 and ImageNet, where we achieve a 46.72% reduction in FLOPs with only a 1.25% loss in accuracy.
  arXiv  Detail & Related papers  (2023-09-29T13:09:40Z)
• Amplitude-Varying Perturbation for Balancing Privacy and Utility in Federated Learning [86.08285033925597]
  This paper presents a new DP perturbation mechanism with a time-varying noise amplitude to protect the privacy of federated learning. We derive an online refinement of the series to prevent FL from premature convergence resulting from excessive perturbation noise. The contribution of the new DP mechanism to the convergence and accuracy of privacy-preserving FL is corroborated, compared to the state-of-the-art Gaussian noise mechanism with a persistent noise amplitude.
  arXiv  Detail & Related papers  (2023-03-07T22:52:40Z)
• Audio-Visual Efficient Conformer for Robust Speech Recognition [91.3755431537592]
  We propose to improve the noise of the recently proposed Efficient Conformer Connectionist Temporal Classification architecture by processing both audio and visual modalities. Our experiments show that using audio and visual modalities allows to better recognize speech in the presence of environmental noise and significantly accelerate training, reaching lower WER with 4 times less training steps.
  arXiv  Detail & Related papers  (2023-01-04T05:36:56Z)
• FedCorr: Multi-Stage Federated Learning for Label Noise Correction [80.9366438220228]
  Federated learning (FL) is a privacy-preserving distributed learning paradigm that enables clients to jointly train a global model. We propose $textttFedCorr$, a general multi-stage framework to tackle heterogeneous label noise in FL. Experiments conducted on CIFAR-10/100 with federated synthetic label noise, and on a real-world noisy dataset, Clothing1M, demonstrate that $textttFedCorr$ is robust to label noise.
  arXiv  Detail & Related papers  (2022-04-10T12:51:18Z)
• Raw Waveform Encoder with Multi-Scale Globally Attentive Locally Recurrent Networks for End-to-End Speech Recognition [45.858039215825656]
  We propose a new encoder that adopts globally attentive locally recurrent (GALR) networks and directly takes raw waveform as input. Experiments are conducted on a benchmark dataset AISHELL-2 and two large-scale Mandarin speech corpus of 5,000 hours and 21,000 hours.
  arXiv  Detail & Related papers  (2021-06-08T12:12:33Z)
• Conditioning Trick for Training Stable GANs [70.15099665710336]
  We propose a conditioning trick, called difference departure from normality, applied on the generator network in response to instability issues during GAN training. We force the generator to get closer to the departure from normality function of real samples computed in the spectral domain of Schur decomposition.
  arXiv  Detail & Related papers  (2020-10-12T16:50:22Z)
• End-to-End Multi-speaker Speech Recognition with Transformer [88.22355110349933]
  We replace the RNN-based encoder-decoder in the speech recognition model with a Transformer architecture. We also modify the self-attention component to be restricted to a segment rather than the whole sequence in order to reduce computation.
  arXiv  Detail & Related papers  (2020-02-10T16:29:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.