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This book provides a summary of the manifold audio- and web-based
approaches to music information retrieval (MIR) research. In
contrast to other books dealing solely with music signal
processing, it addresses additional cultural and listener-centric
aspects and thus provides a more holistic view. Consequently, the
text includes methods operating on features extracted directly from
the audio signal, as well as methods operating on features
extracted from contextual information, either the cultural context
of music as represented on the web or the user and usage context of
music. Following the prevalent document-centered paradigm of
information retrieval, the book addresses models of music
similarity that extract computational features to describe an
entity that represents music on any level (e.g., song, album, or
artist), and methods to calculate the similarity between them.
While this perspective and the representations discussed cannot
describe all musical dimensions, they enable us to effectively find
music of similar qualities by providing abstract summarizations of
musical artifacts from different modalities. The text at hand
provides a comprehensive and accessible introduction to the topics
of music search, retrieval, and recommendation from an academic
perspective. It will not only allow those new to the field to
quickly access MIR from an information retrieval point of view but
also raise awareness for the developments of the music domain
within the greater IR community. In this regard, Part I deals with
content-based MIR, in particular the extraction of features from
the music signal and similarity calculation for content-based
retrieval. Part II subsequently addresses MIR methods that make use
of the digitally accessible cultural context of music. Part III
addresses methods of collaborative filtering and user-aware and
multi-modal retrieval, while Part IV explores current and future
applications of music retrieval and recommendation.>
This book provides a summary of the manifold audio- and web-based
approaches to music information retrieval (MIR) research. In
contrast to other books dealing solely with music signal
processing, it addresses additional cultural and listener-centric
aspects and thus provides a more holistic view. Consequently, the
text includes methods operating on features extracted directly from
the audio signal, as well as methods operating on features
extracted from contextual information, either the cultural context
of music as represented on the web or the user and usage context of
music. Following the prevalent document-centered paradigm of
information retrieval, the book addresses models of music
similarity that extract computational features to describe an
entity that represents music on any level (e.g., song, album, or
artist), and methods to calculate the similarity between them.
While this perspective and the representations discussed cannot
describe all musical dimensions, they enable us to effectively find
music of similar qualities by providing abstract summarizations of
musical artifacts from different modalities. The text at hand
provides a comprehensive and accessible introduction to the topics
of music search, retrieval, and recommendation from an academic
perspective. It will not only allow those new to the field to
quickly access MIR from an information retrieval point of view but
also raise awareness for the developments of the music domain
within the greater IR community. In this regard, Part I deals with
content-based MIR, in particular the extraction of features from
the music signal and similarity calculation for content-based
retrieval. Part II subsequently addresses MIR methods that make use
of the digitally accessible cultural context of music. Part III
addresses methods of collaborative filtering and user-aware and
multi-modal retrieval, while Part IV explores current and future
applications of music retrieval and recommendation.>
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Web Engineering - 22nd International Conference, ICWE 2022, Bari, Italy, July 5-8, 2022, Proceedings (Paperback, 1st ed. 2022)
Tommaso Di Noia, In-Young Ko, Markus Schedl, Carmelo Ardito
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R2,776
Discovery Miles 27 760
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Ships in 10 - 15 working days
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This book constitutes the thoroughly refereed proceedings of the
22nd International Conference on Web Engineering, ICWE 2022, held
in Bari, Italy, in July 2022. The 23 revised full papers and 5
short papers presented were carefully reviewed and selected from 81
submissions. The books also contains 6 demonstration and poster
papers, 7 symposium and 5 tutorial papers. They are organized in
topical sections named: recommender systems based on web
technology; social web applications; web applications modelling and
engineering; web big data and web data analytics; web mining and
knowledge extraction; web security and privacy; web user
interfaces.
Music Information Retrieval surveys the young but established field
of research that is Music Information Retrieval (MIR). In doing so,
it pays particular attention to the latest developments in MIR,
such as semantic auto-tagging and user-centric retrieval and
recommendation approaches. It starts by reviewing the
well-established and proven methods for feature extraction and
music indexing, from both the audio signal and contextual data
sources about music items, such as web pages or collaborative tags.
These in turn enable a wide variety of music retrieval tasks, such
as semantic music search or music identification ("query by
example""). Subsequently, it elaborates on the current work on user
analysis and modeling in the context of music recommendation and
retrieval, addressing the recent trend towards user-centric and
adaptive approaches and systems. A discussion follows about the
important aspect of how various MIR approaches to different
problems are evaluated and compared. It concludes with a discussion
about the major open challenges facing MIR.
Music-related metadata is becoming more and more important in times
of digital music distribution. Methods for automatically extracting
such information from the WWW have been elaborated, implemented,
and analyzed. On sets of Web pages that are related to a music
artist or band, Web content mining techniques are applied to
address the following categories of information: similarities
between music artists, prototypicality of an artist for a genre,
descriptive properties of an artist, band members and
instrumentation, images of album cover artwork. Different
approaches to retrieve the corresponding pieces of information for
each of these categories have been elaborated and evaluated
thoroughly on a considerable variety of music repositories.
Moreover, visualization methods and user interaction models for
prototypical and similar artists as well as for descriptive terms
will be presented. Based on the insights gained by the conducted
experiments, the core application of this thesis, the Automatically
Generated Music Information System (AGMIS) was build. AGMIS
demonstrates the applicability of the elaborated techniques on a
large collection of more than 600,000 artists.
Personalized recommender systems have become indispensable in
today's online world. Most of today's recommendation algorithms are
data-driven and based on behavioral data. While such systems can
produce useful recommendations, they are often uninterpretable,
black-box models that do not incorporate the underlying cognitive
reasons for user behavior in the algorithms' design. This survey
presents a thorough review of the state of the art of recommender
systems that leverage psychological constructs and theories to
model and predict user behavior and improve the recommendation
process - so-called psychology-informed recommender systems. The
survey identifies three categories of psychology-informed
recommender systems: cognition-inspired, personality-aware, and
affect-aware recommender systems. For each category, the authors
highlight domains in which psychological theory plays a key role.
Further, they discuss selected decision-psychological phenomena
that impact the interaction between a user and a recommender. They
also focus on related work that investigates the evaluation of
recommender systems from the user perspective and highlight
user-centric evaluation frameworks, and potential research tasks
for future work at the end of this survey.
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