2,855 research outputs found
Neural networks in geophysical applications
Neural networks are increasingly popular in geophysics.
Because they are universal approximators, these
tools can approximate any continuous function with an
arbitrary precision. Hence, they may yield important
contributions to finding solutions to a variety of geophysical applications.
However, knowledge of many methods and techniques
recently developed to increase the performance
and to facilitate the use of neural networks does not seem
to be widespread in the geophysical community. Therefore,
the power of these tools has not yet been explored to
their full extent. In this paper, techniques are described
for faster training, better overall performance, i.e., generalization,and the automatic estimation of network size
and architecture
Classification of Occluded Objects using Fast Recurrent Processing
Recurrent neural networks are powerful tools for handling incomplete data
problems in computer vision, thanks to their significant generative
capabilities. However, the computational demand for these algorithms is too
high to work in real time, without specialized hardware or software solutions.
In this paper, we propose a framework for augmenting recurrent processing
capabilities into a feedforward network without sacrificing much from
computational efficiency. We assume a mixture model and generate samples of the
last hidden layer according to the class decisions of the output layer, modify
the hidden layer activity using the samples, and propagate to lower layers. For
visual occlusion problem, the iterative procedure emulates feedforward-feedback
loop, filling-in the missing hidden layer activity with meaningful
representations. The proposed algorithm is tested on a widely used dataset, and
shown to achieve 2 improvement in classification accuracy for occluded
objects. When compared to Restricted Boltzmann Machines, our algorithm shows
superior performance for occluded object classification.Comment: arXiv admin note: text overlap with arXiv:1409.8576 by other author
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
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