18,931 research outputs found
Neural Networks for improved signal source enumeration and localization with unsteered antenna arrays
Direction of Arrival estimation using unsteered antenna arrays, unlike mechanically scanned or phased arrays, requires complex algorithms which perform poorly with small aperture arrays or without a large number of observations, or snapshots. In general, these algorithms compute a sample covriance matrix to obtain the direction of arrival and some require a prior estimate of the number of signal sources. Herein, artificial neural network architectures are proposed which demonstrate improved estimation of the number of signal sources, the true signal covariance matrix, and the direction of arrival. The proposed number of source estimation network demonstrates robust performance in the case of coherent signals where conventional methods fail. For covariance matrix estimation, four different network architectures are assessed and the best performing architecture achieves a 20 times improvement in performance over the sample covariance matrix. Additionally, this network can achieve comparable performance to the sample covariance matrix with 1/8-th the amount of snapshots. For direction of arrival estimation, preliminary results are provided comparing six architectures which all demonstrate high levels of accuracy and demonstrate the benefits of progressively training artificial neural networks by training on a sequence of sub- problems and extending to the network to encapsulate the entire process
Localizing Spatial Information in Neural Spatiospectral Filters
Beamforming for multichannel speech enhancement relies on the estimation of
spatial characteristics of the acoustic scene. In its simplest form, the
delay-and-sum beamformer (DSB) introduces a time delay to all channels to align
the desired signal components for constructive superposition. Recent
investigations of neural spatiospectral filtering revealed that these filters
can be characterized by a beampattern similar to one of traditional
beamformers, which shows that artificial neural networks can learn and
explicitly represent spatial structure. Using the Complex-valued Spatial
Autoencoder (COSPA) as an exemplary neural spatiospectral filter for
multichannel speech enhancement, we investigate where and how such networks
represent spatial information. We show via clustering that for COSPA the
spatial information is represented by the features generated by a gated
recurrent unit (GRU) layer that has access to all channels simultaneously and
that these features are not source -- but only direction of arrival-dependent.Comment: Submitted to the 31st European Signal Processing Conference (EUSIPCO
2023), Helsinki, Finland. 5 pages, 3 figure
Model Order Selection in DoA Scenarios via Cross-Entropy based Machine Learning Techniques
In this paper, we present a machine learning approach for estimating the
number of incident wavefronts in a direction of arrival scenario. In contrast
to previous works, a multilayer neural network with a cross-entropy objective
is trained. Furthermore, we investigate an online training procedure that
allows an adaption of the neural network to imperfections of an antenna array
without explicitly calibrating the array manifold. We show via simulations that
the proposed method outperforms classical model order selection schemes based
on information criteria in terms of accuracy, especially for a small number of
snapshots and at low signal-to-noise-ratios. Also, the online training
procedure enables the neural network to adapt with only a few online training
samples, if initialized by offline training on artificial data
Machine learning approach for detection of nonTor traffic
Intrusion detection has attracted a considerable interest from researchers and industry. After many years of research the community still faces the problem of building reliable and efficient intrusion detection systems (IDS) capable of handling large quantities of data with changing patterns in real time situations. The Tor network is popular in providing privacy and security to end user by anonymizing the identity of internet users connecting through a series of tunnels and nodes. This work identifies two problems; classification of Tor traffic and nonTor traffic to expose the activities within Tor traffic that minimizes the protection of users in using the UNB-CIC Tor Network Traffic dataset and classification of the Tor traffic flow in the network. This paper proposes a hybrid classifier; Artificial Neural Network in conjunction with Correlation feature selection algorithm for dimensionality reduction and improved classification performance. The reliability and efficiency of the propose hybrid classifier is compared with Support Vector Machine and naïve Bayes classifiers in detecting nonTor traffic in UNB-CIC Tor Network Traffic dataset. Experimental results show the hybrid classifier, ANN-CFS proved a better classifier in detecting nonTor traffic and classifying the Tor traffic flow in UNB-CIC Tor Network Traffic dataset
RF Localization in Indoor Environment
In this paper indoor localization system based on the RF power measurements of the Received Signal Strength (RSS) in WLAN environment is presented. Today, the most viable solution for localization is the RSS fingerprinting based approach, where in order to establish a relationship between RSS values and location, different machine learning approaches are used. The advantage of this approach based on WLAN technology is that it does not need new infrastructure (it reuses already and widely deployed equipment), and the RSS measurement is part of the normal operating mode of wireless equipment. We derive the Cramer-Rao Lower Bound (CRLB) of localization accuracy for RSS measurements. In analysis of the bound we give insight in localization performance and deployment issues of a localization system, which could help designing an efficient localization system. To compare different machine learning approaches we developed a localization system based on an artificial neural network, k-nearest neighbors, probabilistic method based on the Gaussian kernel and the histogram method. We tested the developed system in real world WLAN indoor environment, where realistic RSS measurements were collected. Experimental comparison of the results has been investigated and average location estimation error of around 2 meters was obtained
Towards End-to-End Acoustic Localization using Deep Learning: from Audio Signal to Source Position Coordinates
This paper presents a novel approach for indoor acoustic source localization
using microphone arrays and based on a Convolutional Neural Network (CNN). The
proposed solution is, to the best of our knowledge, the first published work in
which the CNN is designed to directly estimate the three dimensional position
of an acoustic source, using the raw audio signal as the input information
avoiding the use of hand crafted audio features. Given the limited amount of
available localization data, we propose in this paper a training strategy based
on two steps. We first train our network using semi-synthetic data, generated
from close talk speech recordings, and where we simulate the time delays and
distortion suffered in the signal that propagates from the source to the array
of microphones. We then fine tune this network using a small amount of real
data. Our experimental results show that this strategy is able to produce
networks that significantly improve existing localization methods based on
\textit{SRP-PHAT} strategies. In addition, our experiments show that our CNN
method exhibits better resistance against varying gender of the speaker and
different window sizes compared with the other methods.Comment: 18 pages, 3 figures, 8 table
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