TY - JOUR
T1 - Automatic music transcription and audio source separation
AU - Plumbley, M. D.
AU - Abdallah, S. A.
AU - Bello, J. P.
AU - Davies, M. E.
AU - Monti, G.
AU - Sandler, M. B.
N1 - Funding Information:
S. AbdalhliasspudopbyrantEPeCSPRoaadste Studgtnuert.JsP.hB.oiaepdlnl G. Monti are supportdeby the OMRAS project, jointly funded by JISC (UK) and NSF (USA). The music sequences used to generate Figure 6(a) and (c) are used by permission from theCslslPaincoiMaaiidPagehttp:==wwiwnao-midi.de.p ,CohtbypBernydKruergierg. Address correspondence to Dr. M. D. Plumbley, Department of Electronic Engieenr-ing, Queen Mary, University of London, Mile End Road, London El 4NS, United Kingdom. E-mail: [email protected]
PY - 2002/9
Y1 - 2002/9
N2 - In this article, we give an overview of a range of approaches to the analysis and separation of musical audio. In particular, we consider the problems of automatic music transcription and audio source separation, which are of particular interest to our group. Monophonic music transcription, where a single note is present at one time, can be tackled using an autocorrelation-based method. For polyphonic music transcription, with several notes at any time, other approaches can be used, such as blackboard model or a multiple-cause/sparse coding method. The latter is based on ideas and methods related to independent component analysis (ICA), a method for sound source separation.
AB - In this article, we give an overview of a range of approaches to the analysis and separation of musical audio. In particular, we consider the problems of automatic music transcription and audio source separation, which are of particular interest to our group. Monophonic music transcription, where a single note is present at one time, can be tackled using an autocorrelation-based method. For polyphonic music transcription, with several notes at any time, other approaches can be used, such as blackboard model or a multiple-cause/sparse coding method. The latter is based on ideas and methods related to independent component analysis (ICA), a method for sound source separation.
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U2 - 10.1080/01969720290040777
DO - 10.1080/01969720290040777
M3 - Article
AN - SCOPUS:0036743278
SN - 0196-9722
VL - 33
SP - 603
EP - 627
JO - Cybernetics and Systems
JF - Cybernetics and Systems
IS - 6
ER -