2,410 research outputs found
Basic Filters for Convolutional Neural Networks Applied to Music: Training or Design?
When convolutional neural networks are used to tackle learning problems based
on music or, more generally, time series data, raw one-dimensional data are
commonly pre-processed to obtain spectrogram or mel-spectrogram coefficients,
which are then used as input to the actual neural network. In this
contribution, we investigate, both theoretically and experimentally, the
influence of this pre-processing step on the network's performance and pose the
question, whether replacing it by applying adaptive or learned filters directly
to the raw data, can improve learning success. The theoretical results show
that approximately reproducing mel-spectrogram coefficients by applying
adaptive filters and subsequent time-averaging is in principle possible. We
also conducted extensive experimental work on the task of singing voice
detection in music. The results of these experiments show that for
classification based on Convolutional Neural Networks the features obtained
from adaptive filter banks followed by time-averaging perform better than the
canonical Fourier-transform-based mel-spectrogram coefficients. Alternative
adaptive approaches with center frequencies or time-averaging lengths learned
from training data perform equally well.Comment: Completely revised version; 21 pages, 4 figure
Neonatal Seizure Detection using Convolutional Neural Networks
This study presents a novel end-to-end architecture that learns hierarchical
representations from raw EEG data using fully convolutional deep neural
networks for the task of neonatal seizure detection. The deep neural network
acts as both feature extractor and classifier, allowing for end-to-end
optimization of the seizure detector. The designed system is evaluated on a
large dataset of continuous unedited multi-channel neonatal EEG totaling 835
hours and comprising of 1389 seizures. The proposed deep architecture, with
sample-level filters, achieves an accuracy that is comparable to the
state-of-the-art SVM-based neonatal seizure detector, which operates on a set
of carefully designed hand-crafted features. The fully convolutional
architecture allows for the localization of EEG waveforms and patterns that
result in high seizure probabilities for further clinical examination.Comment: IEEE International Workshop on Machine Learning for Signal Processin
DART: Distribution Aware Retinal Transform for Event-based Cameras
We introduce a generic visual descriptor, termed as distribution aware
retinal transform (DART), that encodes the structural context using log-polar
grids for event cameras. The DART descriptor is applied to four different
problems, namely object classification, tracking, detection and feature
matching: (1) The DART features are directly employed as local descriptors in a
bag-of-features classification framework and testing is carried out on four
standard event-based object datasets (N-MNIST, MNIST-DVS, CIFAR10-DVS,
NCaltech-101). (2) Extending the classification system, tracking is
demonstrated using two key novelties: (i) For overcoming the low-sample problem
for the one-shot learning of a binary classifier, statistical bootstrapping is
leveraged with online learning; (ii) To achieve tracker robustness, the scale
and rotation equivariance property of the DART descriptors is exploited for the
one-shot learning. (3) To solve the long-term object tracking problem, an
object detector is designed using the principle of cluster majority voting. The
detection scheme is then combined with the tracker to result in a high
intersection-over-union score with augmented ground truth annotations on the
publicly available event camera dataset. (4) Finally, the event context encoded
by DART greatly simplifies the feature correspondence problem, especially for
spatio-temporal slices far apart in time, which has not been explicitly tackled
in the event-based vision domain.Comment: 12 pages, revision submitted to TPAMI in Nov 201
Duration mismatch compensation using four-covariance model and deep neural network for speaker verification
International audienceDuration mismatch between enrollment and test utterances still remains a major concern for reliability of real-life speaker recognition applications. Two approaches are proposed here to deal with this case when using the i-vector representation. The first one is an adaptation of Gaussian Probabilistic Linear Discriminant Analysis (PLDA) modeling, which can be extended to the case of any shift between i-vectors drawn from two distinct distributions. The second one attempts to map i-vectors of truncated segments of an utterance to the i-vector of the full segment, by the use of deep neural networks (DNN). Our results show that both new approaches outperform the standard PLDA by about 10 % relative, noting that these back-end methods could complement those quantifying the i-vector uncertainty during its extraction process, in the case of duration gap
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