11,695 research outputs found
Modelling of Sound Events with Hidden Imbalances Based on Clustering and Separate Sub-Dictionary Learning
This paper proposes an effective modelling of sound event spectra with a
hidden data-size-imbalance, for improved Acoustic Event Detection (AED). The
proposed method models each event as an aggregated representation of a few
latent factors, while conventional approaches try to find acoustic elements
directly from the event spectra. In the method, all the latent factors across
all events are assigned comparable importance and complexity to overcome the
hidden imbalance of data-sizes in event spectra. To extract latent factors in
each event, the proposed method employs clustering and performs non-negative
matrix factorization to each latent factor, and learns its acoustic elements as
a sub-dictionary. Separate sub-dictionary learning effectively models the
acoustic elements with limited data-sizes and avoids over-fitting due to hidden
imbalances in training data. For the task of polyphonic sound event detection
from DCASE 2013 challenge, an AED based on the proposed modelling achieves a
detection F-measure of 46.5%, a significant improvement of more than 19% as
compared to the existing state-of-the-art methods
Constrained Deep Transfer Feature Learning and its Applications
Feature learning with deep models has achieved impressive results for both
data representation and classification for various vision tasks. Deep feature
learning, however, typically requires a large amount of training data, which
may not be feasible for some application domains. Transfer learning can be one
of the approaches to alleviate this problem by transferring data from data-rich
source domain to data-scarce target domain. Existing transfer learning methods
typically perform one-shot transfer learning and often ignore the specific
properties that the transferred data must satisfy. To address these issues, we
introduce a constrained deep transfer feature learning method to perform
simultaneous transfer learning and feature learning by performing transfer
learning in a progressively improving feature space iteratively in order to
better narrow the gap between the target domain and the source domain for
effective transfer of the data from the source domain to target domain.
Furthermore, we propose to exploit the target domain knowledge and incorporate
such prior knowledge as a constraint during transfer learning to ensure that
the transferred data satisfies certain properties of the target domain. To
demonstrate the effectiveness of the proposed constrained deep transfer feature
learning method, we apply it to thermal feature learning for eye detection by
transferring from the visible domain. We also applied the proposed method for
cross-view facial expression recognition as a second application. The
experimental results demonstrate the effectiveness of the proposed method for
both applications.Comment: International Conference on Computer Vision and Pattern Recognition,
201
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