(to be announced)
Speaker: Beth Logan, Hewlett-Packard, Cambridge Research Lab, Cambridge, Ma
When: 2004-01-28 11:00:00
Venue: 78-420
Host: Vaughan Clarkson
Abstract:The quantity of music available ubiquitously is growing rapidly.
There is thus a need for analysis techniques to automatically
organize vast audio repositories. In this talk, we describe several
of our efforts in this direction: automatic music summarization and
automatically determining music similarity. Our approaches bring
together knowledge from the signal processing, machine learning and
information retrieval fields.
Both of our techniques extract spectral features from each song and
learn statistical models of these. Our music summarization technique
then automatically chooses a representative phrase for each song
using the segmentation provided by its model. Our music similarity
technique compares the models for each pair of songs using the Earth
Mover's Distance (Rubner1998) to form a distance matrix. Both
approaches show great promise, evidenced by objective and subjective
results and demonstrations.
Biography:Beth Logan received the BSc. and B.E. degrees from the University of
Queensland, Australia, in 1990 and 1991 respectively. She received
the PhD in engineering from the University of Cambridge, United
Kingdom, in 1998, completing a dissertation on speech
enhancement. Since 1998, she has been a research scientist at HP Labs
(formerly Digital) in Cambridge Massachusetts. Her work here has
focused on scalable organization of digital content, primarily
looking at indexing and modeling of speech and music.
Type: ITEE Seminar
Contact:Vaughan Clarkson, seminar host (v.clarkson@itee.uq.edu.au)
or Guido Governatori (ITEE seminar co-ordinator)
(guido@itee.uq.edu.au)