Campus Event Calendar
Event Entry
New for: D1, D2, D3, D4, D5
What and Who
Multimedia Retrieval - Music and Motion
Meinard Müller
Max-Planck-Institut für Informatik - D4
Senior Researcher Series
AG 1, AG 2, AG 3, AG 4, AG 5, SWS, RG1, RG2
MPI Audience
English
Note: We use this to send email in the morning.
Date, Time and Location
Wednesday, 6 February 2008
16:00
45 Minutes
E1 4
024
Saarbrücken
Abstract
In this talk, we introduce concepts and algorithms for robust
and efficient multimedia retrieval in the presence of
variations. By means of two different types of multimedia data
-- waveform-based music data and human motion data -- we
discuss strategies for handling object deformations and
variability in the given data. To illustrate the kind of
problems encountered in content-based retrieval, we outline
some typical query-by-example scenarios.
In the music domain, one often has multiple realizations of one
and the same piece of music such as audio recordings of
different interpretations and arrangements. Given an excerpt of
a specific audio recording as a query, say, the first twenty
seconds of Bernstein's interpretation of Beethoven's Fifth
Symphony, the objective is to find all corresponding audio
clips within a given music database. In case of Beethoven's
Fifth, this includes the repetition of the theme in the
exposition or in the recapitulation within the same
interpretation, as well as the corresponding excerpts in all
recordings of the same piece conducted, e. g. by Toscanini or
Karajan. Even more challenging is to also include arrangements
such as Liszt's piano transcription of Beethoven's Fifth or a
synthesized version of a corresponding MIDI file. The main
difficulty in such a matching scenario is that two audio clips,
even though similar from a musical point of view, may exhibit
significant variations in dynamics, timbre, execution of note
groups, musical key, articulation, or tempo.
Switching to the motion domain, we consider a motion capture
database containing a variety of human motions performed by
different actors in various styles. Then, given a short motion
clip as a query, the task is to automatically locate all
database motion fragments that are in some sense similar to the
query. For example, querying for a kicking motion, one may want
to retrieve all database kicking motions irrespective of the
specific motion speed or the direction and height of the kick.
Here, the variations that are to be handled in the retrieval.
and efficient multimedia retrieval in the presence of
variations. By means of two different types of multimedia data
-- waveform-based music data and human motion data -- we
discuss strategies for handling object deformations and
variability in the given data. To illustrate the kind of
problems encountered in content-based retrieval, we outline
some typical query-by-example scenarios.
In the music domain, one often has multiple realizations of one
and the same piece of music such as audio recordings of
different interpretations and arrangements. Given an excerpt of
a specific audio recording as a query, say, the first twenty
seconds of Bernstein's interpretation of Beethoven's Fifth
Symphony, the objective is to find all corresponding audio
clips within a given music database. In case of Beethoven's
Fifth, this includes the repetition of the theme in the
exposition or in the recapitulation within the same
interpretation, as well as the corresponding excerpts in all
recordings of the same piece conducted, e. g. by Toscanini or
Karajan. Even more challenging is to also include arrangements
such as Liszt's piano transcription of Beethoven's Fifth or a
synthesized version of a corresponding MIDI file. The main
difficulty in such a matching scenario is that two audio clips,
even though similar from a musical point of view, may exhibit
significant variations in dynamics, timbre, execution of note
groups, musical key, articulation, or tempo.
Switching to the motion domain, we consider a motion capture
database containing a variety of human motions performed by
different actors in various styles. Then, given a short motion
clip as a query, the task is to automatically locate all
database motion fragments that are in some sense similar to the
query. For example, querying for a kicking motion, one may want
to retrieve all database kicking motions irrespective of the
specific motion speed or the direction and height of the kick.
Here, the variations that are to be handled in the retrieval.
Contact
Roxane Wetzel
900
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Roxane Wetzel, 02/06/2008 08:55
Roxane Wetzel, 01/10/2008 13:39
Roxane Wetzel, 12/20/2007 09:45
Roxane Wetzel, 12/20/2007 09:44 -- Created document.
Roxane Wetzel, 01/10/2008 13:39
Roxane Wetzel, 12/20/2007 09:45
Roxane Wetzel, 12/20/2007 09:44 -- Created document.