8,631 research outputs found
Deep CNN Framework for Audio Event Recognition using Weakly Labeled Web Data
The development of audio event recognition models requires labeled training
data, which are generally hard to obtain. One promising source of recordings of
audio events is the large amount of multimedia data on the web. In particular,
if the audio content analysis must itself be performed on web audio, it is
important to train the recognizers themselves from such data. Training from
these web data, however, poses several challenges, the most important being the
availability of labels : labels, if any, that may be obtained for the data are
generally {\em weak}, and not of the kind conventionally required for training
detectors or classifiers. We propose that learning algorithms that can exploit
weak labels offer an effective method to learn from web data. We then propose a
robust and efficient deep convolutional neural network (CNN) based framework to
learn audio event recognizers from weakly labeled data. The proposed method can
train from and analyze recordings of variable length in an efficient manner and
outperforms a network trained with {\em strongly labeled} web data by a
considerable margin
Exploration of Risk Factors Associated with Adolescent Drug Use through Cutting Edge Recursive Partitioning Techniques
How a "Hit" is Born: The Emergence of Popularity from the Dynamics of Collective Choice
In recent times there has been a surge of interest in seeking out patterns in
the aggregate behavior of socio-economic systems. One such domain is the
emergence of statistical regularities in the evolution of collective choice
from individual behavior. This is manifested in the sudden emergence of
popularity or "success" of certain ideas or products, compared to their
numerous, often very similar, competitors. In this paper, we present an
empirical study of a wide range of popularity distributions, spanning from
scientific paper citations to movie gross income. Our results show that in the
majority of cases, the distribution follows a log-normal form, suggesting that
multiplicative stochastic processes are the basis for emergence of popular
entities. This suggests the existence of some general principles of complex
organization leading to the emergence of popularity. We discuss the theoretical
principles needed to explain this socio-economic phenomenon, and present a
model for collective behavior that exhibits bimodality, which has been observed
in certain empirical popularity distributions.Comment: 17 pages, 14 figures, A version of the work is published in
Econophysics and Sociophysics: Trends and Perspectives, (eds.) Bikas K.
Chakrabarti, Anirban Chakraborti, Arnab Chatterjee; Wiley-VCH, Berlin (2006);
Chapter-15, pages: 417-44
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