21,046 research outputs found
Design of a Novel Antenna Array Beamformer Using Neural Networks Trained by Modified Adaptive Dispersion Invasive Weed Optimization Based Data
A new antenna array beamformer based on neural networks (NNs) is presented. The NN training is performed by using optimized data sets extracted by a novel Invasive Weed Optimization (IWO) variant called Modified Adaptive Dispersion IWO (MADIWO). The trained NN is utilized as an adaptive beamformer that makes a uniform linear antenna array steer the main lobe towards a desired signal, place respective nulls towards several interference signals and suppress the side lobe level (SLL). Initially, the NN structure is selected by training several NNs of various structures using MADIWO based data and by making a comparison among the NNs in terms of training performance. The selected NN structure is then used to construct an adaptive beamformer, which is compared to MADIWO based and ADIWO based beamformers, regarding the SLL as well as the ability to properly steer the main lobe and the nulls. The comparison is made considering several sets of random cases with different numbers of interference signals and different power levels of additive zero-mean Gaussian noise. The comparative results exhibit the advantages of the proposed beamformer
Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments
Eliminating the negative effect of non-stationary environmental noise is a
long-standing research topic for automatic speech recognition that stills
remains an important challenge. Data-driven supervised approaches, including
ones based on deep neural networks, have recently emerged as potential
alternatives to traditional unsupervised approaches and with sufficient
training, can alleviate the shortcomings of the unsupervised methods in various
real-life acoustic environments. In this light, we review recently developed,
representative deep learning approaches for tackling non-stationary additive
and convolutional degradation of speech with the aim of providing guidelines
for those involved in the development of environmentally robust speech
recognition systems. We separately discuss single- and multi-channel techniques
developed for the front-end and back-end of speech recognition systems, as well
as joint front-end and back-end training frameworks
End to End Deep Neural Network Frequency Demodulation of Speech Signals
Frequency modulation (FM) is a form of radio broadcasting which is widely
used nowadays and has been for almost a century. We suggest a
software-defined-radio (SDR) receiver for FM demodulation that adopts an
end-to-end learning based approach and utilizes the prior information of
transmitted speech message in the demodulation process. The receiver detects
and enhances speech from the in-phase and quadrature components of its base
band version. The new system yields high performance detection for both
acoustical disturbances, and communication channel noise and is foreseen to
out-perform the established methods for low signal to noise ratio (SNR)
conditions in both mean square error and in perceptual evaluation of speech
quality score
Bias-Free Shear Estimation using Artificial Neural Networks
Bias due to imperfect shear calibration is the biggest obstacle when
constraints on cosmological parameters are to be extracted from large area weak
lensing surveys such as Pan-STARRS-3pi, DES or future satellite missions like
Euclid. We demonstrate that bias present in existing shear measurement
pipelines (e.g. KSB) can be almost entirely removed by means of neural
networks. In this way, bias correction can depend on the properties of the
individual galaxy instead on being a single global value. We present a
procedure to train neural networks for shear estimation and apply this to
subsets of simulated GREAT08 RealNoise data. We also show that circularization
of the PSF before measuring the shear reduces the scatter related to the PSF
anisotropy correction and thus leads to improved measurements, particularly on
low and medium signal-to-noise data. Our results are competitive with the best
performers in the GREAT08 competition, especially for the medium and higher
signal-to-noise sets. Expressed in terms of the quality parameter defined by
GREAT08 we achieve a Q = 40, 140 and 1300 without and 50, 200 and 1300 with
circularization for low, medium and high signal-to-noise data sets,
respectively.Comment: 19 pages, 8 figures; accepted for publication in Ap
Deep Dictionary Learning: A PARametric NETwork Approach
Deep dictionary learning seeks multiple dictionaries at different image
scales to capture complementary coherent characteristics. We propose a method
for learning a hierarchy of synthesis dictionaries with an image classification
goal. The dictionaries and classification parameters are trained by a
classification objective, and the sparse features are extracted by reducing a
reconstruction loss in each layer. The reconstruction objectives in some sense
regularize the classification problem and inject source signal information in
the extracted features. The performance of the proposed hierarchical method
increases by adding more layers, which consequently makes this model easier to
tune and adapt. The proposed algorithm furthermore, shows remarkably lower
fooling rate in presence of adversarial perturbation. The validation of the
proposed approach is based on its classification performance using four
benchmark datasets and is compared to a CNN of similar size
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