Search Results for author:
Found 10 papers, 3 papers with code
Towards Robust Waveform-Based Acoustic Models
We study the problem of learning robust acoustic models in adverse environments, characterized by a significant mismatch between training and test conditions.
When saliency goes off on a tangent: Interpreting Deep Neural Networks with nonlinear saliency maps
However, the success of saliency maps has been at best limited, mainly due to the fact that they interpret the underlying learning system through a linear approximation.
Multiscale Wavelet Transfer Entropy with Application to Corticomuscular Coupling Analysis
Results: Our experiments with neurophysiological signals substantiate the potential of the developed methodologies for detecting and quantifying information flow between EEG and EMG signals for subjects with and without significant CMC or GC, including non-linear cross-frequency interactions, and interactions across different temporal scales.
Goldilocks Neural Networks
We introduce the new "Goldilocks" class of activation functions, which non-linearly deform the input signal only locally when the input signal is in the appropriate range.
ITENE: Intrinsic Transfer Entropy Neural Estimator
Hence, the TE quantifies the improvement, as measured by the log-loss, in the prediction of the target sequence $Y$ that can be accrued when, in addition to the past of $Y$, one also has available past samples from $X$.
Dictionary Learning with BLOTLESS Update
BLOTLESS updates a block of dictionary elements and the corresponding sparse coefficients simultaneously.
Learning Waveform-Based Acoustic Models using Deep Variational Convolutional Neural Networks
We investigate the potential of stochastic neural networks for learning effective waveform-based acoustic models.
Speech Recognition Front End Without Information Loss
Speech representation and modelling in high-dimensional spaces of acoustic waveforms, or a linear transformation thereof, is investigated with the aim of improving the robustness of automatic speech recognition to additive noise.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
A Subband-Based SVM Front-End for Robust ASR
This work proposes a novel support vector machine (SVM) based robust automatic speech recognition (ASR) front-end that operates on an ensemble of the subband components of high-dimensional acoustic waveforms.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Classification of Human Ventricular Arrhythmia in High Dimensional Representation Spaces
We studied classification of human ECGs labelled as normal sinus rhythm, ventricular fibrillation and ventricular tachycardia by means of support vector machines in different representation spaces, using different observation lengths.
