40,108 research outputs found
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
Reservoir Computing Approach to Robust Computation using Unreliable Nanoscale Networks
As we approach the physical limits of CMOS technology, advances in materials
science and nanotechnology are making available a variety of unconventional
computing substrates that can potentially replace top-down-designed
silicon-based computing devices. Inherent stochasticity in the fabrication
process and nanometer scale of these substrates inevitably lead to design
variations, defects, faults, and noise in the resulting devices. A key
challenge is how to harness such devices to perform robust computation. We
propose reservoir computing as a solution. In reservoir computing, computation
takes place by translating the dynamics of an excited medium, called a
reservoir, into a desired output. This approach eliminates the need for
external control and redundancy, and the programming is done using a
closed-form regression problem on the output, which also allows concurrent
programming using a single device. Using a theoretical model, we show that both
regular and irregular reservoirs are intrinsically robust to structural noise
as they perform computation
Band gap prediction for large organic crystal structures with machine learning
Machine-learning models are capable of capturing the structure-property
relationship from a dataset of computationally demanding ab initio
calculations. Over the past two years, the Organic Materials Database (OMDB)
has hosted a growing number of calculated electronic properties of previously
synthesized organic crystal structures. The complexity of the organic crystals
contained within the OMDB, which have on average 82 atoms per unit cell, makes
this database a challenging platform for machine learning applications. In this
paper, the focus is on predicting the band gap which represents one of the
basic properties of a crystalline materials. With this aim, a consistent
dataset of 12 500 crystal structures and their corresponding DFT band gap are
released, freely available for download at https://omdb.mathub.io/dataset. An
ensemble of two state-of-the-art models reach a mean absolute error (MAE) of
0.388 eV, which corresponds to a percentage error of 13% for an average band
gap of 3.05 eV. Finally, the trained models are employed to predict the band
gap for 260 092 materials contained within the Crystallography Open Database
(COD) and made available online so that the predictions can be obtained for any
arbitrary crystal structure uploaded by a user.Comment: 10 pages, 6 figure
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