17,667 research outputs found
Information Retrieval Models
Many applications that handle information on the internet would be completely\ud
inadequate without the support of information retrieval technology. How would\ud
we find information on the world wide web if there were no web search engines?\ud
How would we manage our email without spam filtering? Much of the development\ud
of information retrieval technology, such as web search engines and spam\ud
filters, requires a combination of experimentation and theory. Experimentation\ud
and rigorous empirical testing are needed to keep up with increasing volumes of\ud
web pages and emails. Furthermore, experimentation and constant adaptation\ud
of technology is needed in practice to counteract the effects of people that deliberately\ud
try to manipulate the technology, such as email spammers. However,\ud
if experimentation is not guided by theory, engineering becomes trial and error.\ud
New problems and challenges for information retrieval come up constantly.\ud
They cannot possibly be solved by trial and error alone. So, what is the theory\ud
of information retrieval?\ud
There is not one convincing answer to this question. There are many theories,\ud
here called formal models, and each model is helpful for the development of\ud
some information retrieval tools, but not so helpful for the development others.\ud
In order to understand information retrieval, it is essential to learn about these\ud
retrieval models. In this chapter, some of the most important retrieval models\ud
are gathered and explained in a tutorial style
Affective Music Information Retrieval
Much of the appeal of music lies in its power to convey emotions/moods and to
evoke them in listeners. In consequence, the past decade witnessed a growing
interest in modeling emotions from musical signals in the music information
retrieval (MIR) community. In this article, we present a novel generative
approach to music emotion modeling, with a specific focus on the
valence-arousal (VA) dimension model of emotion. The presented generative
model, called \emph{acoustic emotion Gaussians} (AEG), better accounts for the
subjectivity of emotion perception by the use of probability distributions.
Specifically, it learns from the emotion annotations of multiple subjects a
Gaussian mixture model in the VA space with prior constraints on the
corresponding acoustic features of the training music pieces. Such a
computational framework is technically sound, capable of learning in an online
fashion, and thus applicable to a variety of applications, including
user-independent (general) and user-dependent (personalized) emotion
recognition and emotion-based music retrieval. We report evaluations of the
aforementioned applications of AEG on a larger-scale emotion-annotated corpora,
AMG1608, to demonstrate the effectiveness of AEG and to showcase how
evaluations are conducted for research on emotion-based MIR. Directions of
future work are also discussed.Comment: 40 pages, 18 figures, 5 tables, author versio
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