Making deep neural networks work for medical audio: representation, compression and domain adaptation | Paper Citation Record

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Making deep neural networks work for medical audio: representation, compression and domain adaptation A fully automated approach for baby cry signal segmentation and boundary detection of expi- ratory and inspiratory episodes

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Making deep neural networks work for medical audio: representation, compression and domain adaptation VCMNet: Weakly supervised learning for automatic infant vocalisation maturity analysis

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Tensor Decompositions for Learning Latent VariableModels

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Alzheimer’s disease: Communication patternsinfamilialandsporadicforms

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Making deep neural networks work for medical audio: representation, compression and domain adaptation On the differences between song and speech emotion recognition: Effect of feature sets, feature types, and classi- fiers

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Do deep nets really need to be deep?

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Making deep neural networks work for medical audio: representation, compression and domain adaptation A theory of learning from different domains

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Analysis of Representations for Domain Adaptation

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Greedy Layer-Wise Training of Deep Networks

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Retrieved 3 August 2019

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Making deep neural networks work for medical audio: representation, compression and domain adaptation A training algorithm for optimal margin classifiers

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Application-independent evaluation of speaker detection

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Making deep neural networks work for medical audio: representation, compression and domain adaptation CryCeleb: A Speaker Verification Dataset Based on Infant Cry Sounds

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Dataset shift in machine learning

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Transfer Learning and Data Augmentation Tech- niques to the COVID-19 Identification Tasks in ComParE 2021

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Making deep neural networks work for medical audio: representation, compression and domain adaptation VGGSound: A Large-scale Audio-Visual Dataset

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Making deep neural networks work for medical audio: representation, compression and domain adaptation VGGSound: A Large-scale Audio-Visual Dataset

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Making deep neural networks work for medical audio: representation, compression and domain adaptation WavLM: Large-scale self-supervised pre-training for full stack speech processing

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Making deep neural networks work for medical audio: representation, compression and domain adaptation A simple framework for contrastive learning of visual repre- sentations

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Big self-supervised models are strong semi-supervised learn- ers

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Self-supervised learning based domain adaptation for robust speaker verification

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Making deep neural networks work for medical audio: representation, compression and domain adaptation An exploration of parameter redundancy in deep networks with circulant projections

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Spectral analysis of infant cries and adultspeech

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Learning Phrase Representations using RNN Encoder– Decoder for Statistical Machine Translation

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Making deep neural networks work for medical audio: representation, compression and domain adaptation VoxCeleb2: Deep Speaker Recog- nition

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Triplet Loss for Infant Cry Verification - CryCeleb2023 Solu- tion

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Making deep neural networks work for medical audio: representation, compression and domain adaptation An Algorithm for the Machine Cal- culation of Complex Fourier Series

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Support-Vector Networks

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Cry analysis in in- fantsofnarcoticaddictedmothers

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Making deep neural networks work for medical audio: representation, compression and domain adaptation The infant cry: what can it tell us?

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Effects of in utero cocaine exposure on newborn acoustical cry characteristics

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Approximation by superpositions of a sigmoidal function

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Acoustical analysis of pain cries in neonates: Fundamental frequency

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Speech and swallowing prob- lems in multiple sclerosis

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Differential di- agnostic patterns of dysarthria

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Comparison of Parametric Repre- sentations for Monosyllabic Word Recognition in Continuously Spoken Sen- tences

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Making deep neural networks work for medical audio: representation, compression and domain adaptation A Multilinear Singular Value Decomposition

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Arcface: Additive angular margin loss for deep face recognition

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Predicting parameters in deep learning

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Making deep neural networks work for medical audio: representation, compression and domain adaptation ECAPA-TDNN: Emphasized channel attention, propagation and aggregation in TDNN based speaker verification

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Transfer learning of weakly labelled audio

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Decaf: A deep convolutional activation feature for generic visual recognition

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Emotional expression recognition using support vector ma- chines

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Making deep neural networks work for medical audio: representation, compression and domain adaptation The Approximation of One Matrix by Another of Lower Rank

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Finding Structure in Time

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Why does unsupervised pre-training help deep learn- ing?

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Making deep neural networks work for medical audio: representation, compression and domain adaptation A Classification Model for Infant Cries with Hearing Impairment and Unilateral Cleft Lip and Palate

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Opensmile: the munich versatile and fast open-source audio feature extractor

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Making deep neural networks work for medical audio: representation, compression and domain adaptation TheGenevaminimalisticacousticparameterset(GeMAPS) for voice research and affective computing

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Identification of infants’ cry motivation using spectro- grams

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Feynman, Robert B

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Model-agnosticmeta-learning for fast adaptation of deep networks

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Flanagan

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Auditory Patterns

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Loudness, its definition, measurement and calculation

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Théorie Analytique de la Chaleur

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Training batchnorm and only batchnorm: On the expressive power of random features in CNNs

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Catastrophic forgetting in connectionist networks

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Neocognitron: A Self-organizing Neural Network Model for a Mechanism of Pattern Recognition Unaffected by Shift in Position

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Support vector machines for speech recognition

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Unsupervised Domain Adaptation by Backpropagation

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Domain-adversarial training of neural networks

Reference 62

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Ultimate tensorization: compressing convolutional and fc layers alike

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Audio Set: An ontology and human-labeled dataset for audio events

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Understanding the difficulty of training deep feedforward neural networks

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Deep sparse rectifier neu- ralnetworks

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Making deep neural networks work for medical audio: representation, compression and domain adaptation A Physioacoustic Model of the Infant Cry

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Making deep neural networks work for medical audio: representation, compression and domain adaptation A physioacoustic model of the infant cry

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Making deep neural networks work for medical audio: representation, compression and domain adaptation SSAST: Self-supervised audio spectrogram transformer

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Goodfellow et al.Generative Adversarial Networks

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Self-supervisedlearningforinfantcryanalysis

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Self-supervised learning for infant cry analysis

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Semi-supervised Learning by Entropy Minimization

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Making deep neural networks work for medical audio: representation, compression and domain adaptation A Kernel Two-Sample Test

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Covariate Shift by Kernel Mean Matching

Reference 75

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Don’t stop pretraining: Adapt language models to domains and tasks

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Making deep neural networks work for medical audio: representation, compression and domain adaptation An assessment of par- alinguistic acoustic features for detection of Alzheimer’s dementia in sponta- neous speech

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Emotion recognition in low-resource settings: An evalua- tion of automatic feature selection methods

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding

Reference 79

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Making deep neural networks work for medical audio: representation, compression and domain adaptation On the Use of Windows for Harmonic Analysis with the Discrete Fourier Transform

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Mapping computer science research in Africa: using academic networking sites for assessing research activity

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Rethinkingimagenetpre-training

Reference 82

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Deep residual learning for image recognition

Reference 83

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Delving deep into rectifiers: Surpassing human-level perfor- mance on imagenet classification

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Momentum Contrast for Unsupervised Visual Represen- tation Learning

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Streaming end-to-end speech recognition for mobile de- vices

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Making deep neural networks work for medical audio: representation, compression and domain adaptation End-to-endtext-dependentspeakerverification

Reference 87

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Distilling the Knowledge in a Neural Network

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Making deep neural networks work for medical audio: representation, compression and domain adaptation A Fast Learning Al- gorithm for Deep Belief Nets

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Long short-term memory

Reference 90

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Long Short-Term Memory

Reference 91

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Neural networks and physical systems with emergent col- lective computational abilities

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Universal Language Model Fine-tuning for Text Classification

Reference 93

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Correcting Sample Selection Bias by Unlabeled Data

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Speech disorders in Parkinson’s disease: A clinical and phonetic study

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Batch normalization: Accelerating deep network training by reducing internal covariate shift

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Making deep neural networks work for medical audio: representation, compression and domain adaptation A review of infant cry analysis and classification

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Principal component analysis

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Automated prediction of extubation success in extremely preterm infants: the APEX multicenter study

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Making deep neural networks work for medical audio: representation, compression and domain adaptation Undersampling and bagging of decision trees in the analysisofcardiorespiratorybehaviorforthepredictionofextubationreadiness in extremely preterm infants

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