[PDF] Top 20 Applications of deep learning to speech enhancement.
Has 10000 "Applications of deep learning to speech enhancement." found on our website. Below are the top 20 most common "Applications of deep learning to speech enhancement.".
Applications of deep learning to speech enhancement.
... able to generalize to real room impulse responses even when only trained with simulated room impulse responses, different speakers, and high reverberation ...reduction of perceived reverberation. We ... Voir le document complet
156
Mixture of Inference Networks for VAE-based Audio-visual Speech Enhancement
... speech. Being independent of the noise type at training, these methods show better generalization than the supervised approaches [15], ...quality of speech enhancement [23]–[25], an ... Voir le document complet
12
A deep learning approach to Species Distribution Modelling
... Thanks to f j , we can isolate and understand the effect of the environmental factor x j on the ...form of h j,j 0 is restricted to products, like it is the case in the very popular model ... Voir le document complet
26
Robust Articulatory Speech Synthesis using Deep Neural Networks for BCI Applications
... participated to an intelligibility ...consisted of 10 French vowels /a/, /i/, /u/, /o/, /œ/, /e/, /y/, /ã/, /ɛ̃/, /ɔ̃/, and 30 vowel- consonant-vowel (VCV) pseudo words made of the 10 consonants /p/, ... Voir le document complet
6
Applications of complex numbers to deep neural networks
... for learning unitary matrices and they applied their method on toy tasks and on a real-world speech ...biologically-plausible deep network that allows one to construct richer and more ... Voir le document complet
57
Advances in deep learning methods for speech recognition and understanding
... results of a completed sports match, game, tournament,” “Learn the status of an ongoing sports match, game, ...ing of a given utterance. For the ablation study we report the performance of ... Voir le document complet
108
2020 — Unsupervised speech representation learning
... success of deep learning in speech was obtained with supervised methods, which depend on large amounts of labelled data in the form of text ...many applications where ... Voir le document complet
139
Evaluating the Potential Gain of Auditory and Audiovisual Speech-Predictive Coding Using Deep Learning
... choice of a statistical framework based on deep learning was mo- tivated by its ability to build successive levels of increasingly meaningful abstractions in order to learn and ... Voir le document complet
31
A Recurrent Variational Autoencoder for Speech Enhancement
... on deep neural networks have been extensively used for speech ...try to estimate a clean speech spectrogram or a time-frequency mask from a noisy speech spectrogram, see ...Recently, ... Voir le document complet
8
Sequence to sequence learning and its speech applications
... emergence of gating-based feed-forward neural networks like High- way Networks ( Srivastava et ...one of the most popular and e↵ective strategies for training very deep convolutional neural networks ... Voir le document complet
64
Learning to touch objects through stage-wise deep reinforcement learning
... stage-wise learning procedure which does not require any kinematic models, hand-crafted features, calibration param- eters and needs minimal human ...learns to fixate an object. This implicitly encodes the ... Voir le document complet
8
Deep learning to predict the lab-of-origin of engineered DNA
... input to the CNN is the DNA sequence encoded as a 16,048 × 4 matrix, where the identity of each nt is represented by a one-hot vector ...Ns to avoid positional effects between the ...layer of ... Voir le document complet
11
Deep learning of representations and its application to computer vision
... case of finite data, the maximum likelihood es- timator is not always the best possible ...estimation of parametric models performs poorly compared to other approaches such as Bayesian inference (in ... Voir le document complet
165
Learning to touch objects through stage-wise deep reinforcement learning
... help to discriminate values of close ...pose to design an informative reward in manipulation tasks such as block stacking, reaching and door pushing or ...knowledge of robot kinematics and ... Voir le document complet
7
Deep learning to detect CBC before the merger
... ● Generate different datasets focused on small ranges of SNR ● ~ 3000 whitened curves in each dataset ● Half with a GW. ● Half only pure noise.[r] ... Voir le document complet
12
Deep Learning applied to Road Traffic Speed forecasting
... remark to any time series problem: the CNN seems ill equipped to handle prediction task properly in the case where there is strong causality in the time ...states of the current day (feature map ... Voir le document complet
37
Classification of Hate Speech Using Deep Neural Networks
... spread of hatred that was previously limited to verbal communications has quickly moved over the ...people to discuss and express their opinions are becoming platforms for the dissemination of ... Voir le document complet
12
Deep Learning of Activation Energies
... 2: Deep learning model results. (a) Parity plot of model predictions ...Histogram of prediction errors (predicted minus “true”) for the first ...number of training data points for the ... Voir le document complet
19
Learning to count: A deep learning framework for graphlet count estimation
... aim to apply learning model to learn the mapping from underlying graph structure to the structural attributes, ...Count Learning (GCL) problem, which bridges the graphlet counting and ... Voir le document complet
31
Training and Compensation of Class-conditioned NMF Bases for Speech Enhancement
... Department of Electrical and Computer Engineering, Sherbrooke University, Sherbrooke, Quebec, Canada Abstract In this paper, we introduce a training and compensation algorithm of the class-conditioned basis ... Voir le document complet
14
Sujets connexes