2,412 research outputs found
Block-Online Multi-Channel Speech Enhancement Using DNN-Supported Relative Transfer Function Estimates
This work addresses the problem of block-online processing for multi-channel
speech enhancement. Such processing is vital in scenarios with moving speakers
and/or when very short utterances are processed, e.g., in voice assistant
scenarios. We consider several variants of a system that performs beamforming
supported by DNN-based voice activity detection (VAD) followed by
post-filtering. The speaker is targeted through estimating relative transfer
functions between microphones. Each block of the input signals is processed
independently in order to make the method applicable in highly dynamic
environments. Owing to the short length of the processed block, the statistics
required by the beamformer are estimated less precisely. The influence of this
inaccuracy is studied and compared to the processing regime when recordings are
treated as one block (batch processing). The experimental evaluation of the
proposed method is performed on large datasets of CHiME-4 and on another
dataset featuring moving target speaker. The experiments are evaluated in terms
of objective and perceptual criteria (such as signal-to-interference ratio
(SIR) or perceptual evaluation of speech quality (PESQ), respectively).
Moreover, word error rate (WER) achieved by a baseline automatic speech
recognition system is evaluated, for which the enhancement method serves as a
front-end solution. The results indicate that the proposed method is robust
with respect to short length of the processed block. Significant improvements
in terms of the criteria and WER are observed even for the block length of 250
ms.Comment: 10 pages, 8 figures, 4 tables. Modified version of the article
accepted for publication in IET Signal Processing journal. Original results
unchanged, additional experiments presented, refined discussion and
conclusion
Signal Detection in Ambient Backscatter Systems: Fundamentals, Methods, and Trends
Internet-of-Things (IoT) is rapidly growing in wireless technology, aiming to
connect vast numbers of devices to gather and distribute vital information.
Despite individual devices having low energy consumption, the cumulative demand
results in significant energy usage. Consequently, the concept of
ultra-low-power tags gains appeal. Such tags communicate by reflecting rather
than generating the radio frequency (RF) signals by themselves. Thus, these
backscatter tags can be low-cost and battery-free. The RF signals can be
ambient sources such as wireless-fidelity (Wi-Fi), cellular, or television (TV)
signals, or the system can generate them externally. Backscatter channel
characteristics are different from conventional point-to-point or cooperative
relay channels. These systems are also affected by a strong interference link
between the RF source and the tag besides the direct and backscattering links,
making signal detection challenging. This paper provides an overview of the
fundamentals, challenges, and ongoing research in signal detection for AmBC
networks. It delves into various detection methods, discussing their advantages
and drawbacks. The paper's emphasis on signal detection sets it apart and
positions it as a valuable resource for IoT and wireless communication
professionals and researchers.Comment: Accepted for publication in the IEEE Acces
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)
The implicit objective of the biennial "international - Traveling Workshop on
Interactions between Sparse models and Technology" (iTWIST) is to foster
collaboration between international scientific teams by disseminating ideas
through both specific oral/poster presentations and free discussions. For its
second edition, the iTWIST workshop took place in the medieval and picturesque
town of Namur in Belgium, from Wednesday August 27th till Friday August 29th,
2014. The workshop was conveniently located in "The Arsenal" building within
walking distance of both hotels and town center. iTWIST'14 has gathered about
70 international participants and has featured 9 invited talks, 10 oral
presentations, and 14 posters on the following themes, all related to the
theory, application and generalization of the "sparsity paradigm":
Sparsity-driven data sensing and processing; Union of low dimensional
subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph
sensing/processing; Blind inverse problems and dictionary learning; Sparsity
and computational neuroscience; Information theory, geometry and randomness;
Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?;
Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website:
http://sites.google.com/site/itwist1
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The role of HG in the analysis of temporal iteration and interaural correlation
MSVM Aided Signal Detection in Generalized Spatial Modulation VLC System.
Efficient signal detection technique is developed for generalized spatial modulation (GSM) modulated indoor visible light communication (VLC) system, which is aided by a popular machine learning approach termed as support vector machine (SVM). For general VLC system, maximum likelihood (ML) detector has a high computational complexity, although it is the optimal detection algorithm. In order to alleviate this high computational complexity problem, we take the signal detection task in GSM-VLC system as a multiple classification problem, and propose an efficient signal detection scheme for the considered GSM-VLC system aided by SVM, which has lower computational complexity and nearly optimal detection accuracy. Simulation results demonstrate the efficiency of the proposed SVM aided signal detection technique in the considered indoor GSM-VLC system
CMB-S4 Science Book, First Edition
This book lays out the scientific goals to be addressed by the
next-generation ground-based cosmic microwave background experiment, CMB-S4,
envisioned to consist of dedicated telescopes at the South Pole, the high
Chilean Atacama plateau and possibly a northern hemisphere site, all equipped
with new superconducting cameras. CMB-S4 will dramatically advance cosmological
studies by crossing critical thresholds in the search for the B-mode
polarization signature of primordial gravitational waves, in the determination
of the number and masses of the neutrinos, in the search for evidence of new
light relics, in constraining the nature of dark energy, and in testing general
relativity on large scales
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