2,252 research outputs found
Semi-Supervised Radio Signal Identification
Radio emitter recognition in dense multi-user environments is an important
tool for optimizing spectrum utilization, identifying and minimizing
interference, and enforcing spectrum policy. Radio data is readily available
and easy to obtain from an antenna, but labeled and curated data is often
scarce making supervised learning strategies difficult and time consuming in
practice. We demonstrate that semi-supervised learning techniques can be used
to scale learning beyond supervised datasets, allowing for discerning and
recalling new radio signals by using sparse signal representations based on
both unsupervised and supervised methods for nonlinear feature learning and
clustering methods
Deep Learning for Audio Signal Processing
Given the recent surge in developments of deep learning, this article
provides a review of the state-of-the-art deep learning techniques for audio
signal processing. Speech, music, and environmental sound processing are
considered side-by-side, in order to point out similarities and differences
between the domains, highlighting general methods, problems, key references,
and potential for cross-fertilization between areas. The dominant feature
representations (in particular, log-mel spectra and raw waveform) and deep
learning models are reviewed, including convolutional neural networks, variants
of the long short-term memory architecture, as well as more audio-specific
neural network models. Subsequently, prominent deep learning application areas
are covered, i.e. audio recognition (automatic speech recognition, music
information retrieval, environmental sound detection, localization and
tracking) and synthesis and transformation (source separation, audio
enhancement, generative models for speech, sound, and music synthesis).
Finally, key issues and future questions regarding deep learning applied to
audio signal processing are identified.Comment: 15 pages, 2 pdf figure
The Use of Features Extracted from Noisy Samples for Image Restoration Purposes
An important feature of neural networks is the ability they have to learn from their environment, and, through learning to improve performance in some sense. In the following we restrict the development to the problem of feature extracting unsupervised neural networks derived on the base of the biologically motivated Hebbian self-organizing principle which is conjectured to govern the natural neural assemblies and the classical principal component analysis (PCA) method used by statisticians for almost a century for multivariate data analysis and feature extraction. The research work reported in the paper aims to propose a new image reconstruction method based on the features extracted from the noise given by the principal components of the noise covariance matrix.feature extraction, PCA, Generalized Hebbian Algorithm, image restoration, wavelet transform, multiresolution support set
Decoding Neural Signals with Computational Models: A Systematic Review of Invasive BMI
There are significant milestones in modern human's civilization in which
mankind stepped into a different level of life with a new spectrum of
possibilities and comfort. From fire-lighting technology and wheeled wagons to
writing, electricity and the Internet, each one changed our lives dramatically.
In this paper, we take a deep look into the invasive Brain Machine Interface
(BMI), an ambitious and cutting-edge technology which has the potential to be
another important milestone in human civilization. Not only beneficial for
patients with severe medical conditions, the invasive BMI technology can
significantly impact different technologies and almost every aspect of human's
life. We review the biological and engineering concepts that underpin the
implementation of BMI applications. There are various essential techniques that
are necessary for making invasive BMI applications a reality. We review these
through providing an analysis of (i) possible applications of invasive BMI
technology, (ii) the methods and devices for detecting and decoding brain
signals, as well as (iii) possible options for stimulating signals into human's
brain. Finally, we discuss the challenges and opportunities of invasive BMI for
further development in the area.Comment: 51 pages, 14 figures, review articl
Unsupervised Doppler Radar Based Activity Recognition for e-Healthcare
Passive radio frequency (RF) sensing and monitoring of human daily activities
in elderly care homes is an emerging topic. Micro-Doppler radars are an
appealing solution considering their non-intrusiveness, deep penetration, and
high-distance range. Unsupervised activity recognition using Doppler radar data
has not received attention, in spite of its importance in case of unlabelled or
poorly labelled activities in real scenarios. This study proposes two
unsupervised feature extraction methods for the purpose of human activity
monitoring using Doppler-streams. These include a local Discrete Cosine
Transform (DCT)-based feature extraction method and a local entropy-based
feature extraction method. In addition, a novel application of Convolutional
Variational Autoencoder (CVAE) feature extraction is employed for the first
time for Doppler radar data. The three feature extraction architectures are
compared with the previously used Convolutional Autoencoder (CAE) and linear
feature extraction based on Principal Component Analysis (PCA) and 2DPCA.
Unsupervised clustering is performed using K-Means and K-Medoids. The results
show the superiority of DCT-based method, entropy-based method, and CVAE
features compared to CAE, PCA, and 2DPCA, with more than 5\%-20\% average
accuracy. In regards to computation time, the two proposed methods are
noticeably much faster than the existing CVAE. Furthermore, for
high-dimensional data visualisation, three manifold learning techniques are
considered. The methods are compared for the projection of raw data as well as
the encoded CVAE features. All three methods show an improved visualisation
ability when applied to the encoded CVAE features
Robust Distributed Multi-Source Detection and Labeling in Wireless Acoustic Sensor Networks
The growing demand in complex signal processing methods associated with low-energy large scale wireless acoustic sensor networks (WASNs) urges the shift to a new information and communication technologies (ICT) paradigm.
The emerging research perception aspires for an appealing wireless network communication where multiple heterogeneous devices with different interests can cooperate in various signal processing tasks (MDMT).
Contributions in this doctoral thesis focus on distributed multi-source detection and labeling applied to audio enhancement scenarios pursuing an MDMT fashioned node-specific source-of-interest signal enhancement in WASNs.
In fact, an accurate detection and labeling is a pre-requisite to pursue the MDMT paradigm where nodes in the WASN communicate effectively their sources-of-interest and, therefore, multiple signal processing tasks can be enhanced via cooperation.
First, a novel framework based on a dominant source model in distributed WASNs for resolving the activity detection of multiple speech sources in a reverberant and noisy environment is introduced.
A preliminary rank-one multiplicative non-negative independent component analysis (M-NICA) for unique dominant energy source extraction given associated node clusters is presented.
Partitional algorithms that minimize the within-cluster mean absolute deviation (MAD) and weighted MAD objectives are proposed to determine the cluster membership of the unmixed energies, and thus establish a source specific voice activity recognition.
In a second study, improving the energy signal separation to alleviate the multiple source activity discrimination task is targeted.
Sparsity inducing penalties are enforced on iterative rank-one singular value decomposition layers to extract sparse right rotations.
Then, sparse non-negative blind energy separation is realized using multiplicative updates.
Hence, the multiple source detection problem is converted into a sparse non-negative source energy decorrelation.
Sparsity tunes the supposedly non-active energy signatures to exactly zero-valued energies so that it is easier to identify active energies and an activity detector can be constructed in a straightforward manner.
In a centralized scenario, the activity decision is controlled by a fusion center that delivers the binary source activity detection for every participating energy source.
This strategy gives precise detection results for small source numbers. With a growing number of interfering sources, the distributed detection approach is more promising.
Conjointly, a robust distributed energy separation algorithm for multiple competing sources is proposed.
A robust and regularized -estimation of the covariance matrix of the mixed energies is employed.
This approach yields a simple activity decision using only the robustly unmixed energy signatures of the sources in the WASN.
The performance of the robust activity detector is validated with a distributed adaptive node-specific signal estimation method for speech enhancement.
The latter enhances the quality and intelligibility of the signal while exploiting the accurately estimated multi-source voice decision patterns.
In contrast to the original M-NICA for source separation, the extracted binary activity patterns with the robust energy separation significantly improve the node-specific signal estimation.
Due to the increased computational complexity caused by the additional step of energy signal separation, a new approach to solving the detection question of multi-device multi-source networks is presented.
Stability selection for iterative extraction of robust right singular vectors is considered. The sub-sampling selection technique provides transparency in properly choosing the regularization variable in the Lasso optimization problem.
In this way, the strongest sparse right singular vectors using a robust -norm and stability selection are the set of basis vectors that describe the input data efficiently.
Active/non-active source classification is achieved based on a robust Mahalanobis classifier.
For this, a robust -estimator of the covariance matrix in the Mahalanobis distance is utilized.
Extensive evaluation in centralized and distributed settings is performed to assess the effectiveness of the proposed approach.
Thus, overcoming the computationally demanding source separation scheme is possible via exploiting robust stability selection for sparse multi-energy feature extraction.
With respect to the labeling problem of various sources in a WASN, a robust approach is introduced that exploits the direction-of-arrival of the impinging source signals.
A short-time Fourier transform-based subspace method estimates the angles of locally stationary wide band signals using a uniform linear array.
The median of angles estimated at every frequency bin is utilized to obtain the overall angle for each participating source.
The features, in this case, exploit the similarity across devices in the particular frequency bins that produce reliable direction-of-arrival estimates for each source.
Reliability is defined with respect to the median across frequencies.
All source-specific frequency bands that contribute to correct estimated angles are selected.
A feature vector is formed for every source at each device by storing the frequency bin indices that lie within the upper and lower interval of the median absolute deviation scale of the estimated angle.
Labeling is accomplished by a distributed clustering of the extracted angle-based feature vectors using consensus averaging
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