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Details of Grant 

EPSRC Reference: GR/R54620/01
Title: Automatic Polyphonic Music Transcription Using Multiple Cause Models and Independant Component Analysis.
Principal Investigator: Plumbley, Professor M
Other Investigators:
Davies, Dr M Davies, Professor M Sandler, Professor M
Researcher Co-Investigators:
Professor T Crawford
Project Partners:
Department: Sch of Electronic Eng & Computer Science
Organisation: Queen Mary University of London
Scheme: Standard Research (Pre-FEC)
Starts: 01 January 2002 Ends: 30 June 2004 Value (£): 124,946
EPSRC Research Topic Classifications:
Music & Acoustic Technology
EPSRC Industrial Sector Classifications:
Information Technologies
Related Grants:
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Summary on Grant Application Form
We aim to develop a methodology for automatic transcription of polyphonic music from audio. Our approach is based on the use of adaptive learning systems, specifically multiple-cause model neural networks and independent component analysis (ICA), as data-driven analysis of time-frequency representations. We will use these models to learn note shapes from musical data, rather than relying on experimentally measured characteristics of the human auditory system.In this project we will concentrate on these bottom-up methods, to focus on exactly how much can be extracted from the input data alone, and to complement other work on top-down and heuristic methods taking place in our Lab and elsewhere.As part of this work, we will need to tackle a number of challenges from real-world audio data, such as time-varying spectral shape of instruments, reverberation, nonlinear behaviour of instruments, and noise in audio recordings. To this end, we will use a number of recent developments in multiple cause models and ICA, such as sparse coding, noisy ICA, and nonlinear ICA, and will extend our initial instantaneous analysis to include temporal information.
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