Music Informatics Research Group (MIRG)
Transcript of Music Informatics Research Group (MIRG)
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Music Informatics Research Group
(MIRG)
http://mi.soi.city.ac.uk/
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Music Informatics Research Group (MIRG)
• Formed in 2005
(succeeded the “Centre for Computational Creativity”)
• Members:
– Tillman Weyde (Group leader & Senior lecturer)
– Emmanouil Benetos (Research fellow)
– Daniel Wolff (Research student)
– Reinier DeValk (Research student)
– Srikanth Cherla (Research student)
– Andreas Jansson (Research student)
– Olivier Ruello (Intern)
– Antoine Winckels (Intern)
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Music Informatics Research Group (MIRG)
• Research Activities:
– Music information retrieval
– Music signal analysis
– Computational musicology
– Music knowledge representation
– Applications
• Funding:
– I-MAESTRO
– SLICKMEM
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• Multi-pitch detection, instrument identification, source separation...
• Application: automatic music transcription
• Example: Original audio: Synthesized transcription:
Music Signal Analysis
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Recognition of polyphonic structure and automatic
transcription of lute tablature
Lute tablature: no indication of polyphonic structure Extraction of individual voices using machine learning techniques Transcription into modern music notation
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Music Analysis & Prediction with Neural Networks
• Statistical & probabilistic views on
music cognition
• Music, cognitive science and
machine learning
• Understanding
– Similarity & Style
– Creativity
– Familiarity and Preference
• Music generation
Generative music example:
Image Courtesy, en.wikipedia.org
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Modelling Melodic Similarity
Wavelet Representation for Classification
• Mathematical models for analysing signals
• Allows for analysis at different time-scales
• Works well for recognising similar melodic fragments
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Sequences in Description Logic:
• SEQ representation for
sequences (melodies, chord
progressions) in description logic
• SEQ ontology in OWL
• Extends semantic music
search and reasoning
Representing Musical Structure in the Semantic Web
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Music Similarity Modelling
Music Similarity
• Complex concept but important for
music recommendation and
retrieval
• Depends on many factors
• context, culture,
psychoacoustics ...
Computational Similarity Models
• Weight different aspects of music:
– Which features are important?
– Music perception + Musicology
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Data Collection with Games (With A Purpose)
mi.soi.city.ac.uk/camir/game/
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Data Collection with Games (With A Purpose)
mi.soi.city.ac.uk/camir/game/
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Data Collection with Games (With A Purpose)
mi.soi.city.ac.uk/camir/game/
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Collaborators welcome!
We would like to keep in touch with:
• App developers - interesting app ideas
• Musicians - creative feedback
• Educators - explore application to music education
• Researchers - technical suggestions and discussion
Visit http://mi.soi.city.ac.uk for more info