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Proceedings Paper

Towards automatic music transcription: note extraction based on independent subspace analysis
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Paper Abstract

In this paper we present a technique for the separation of harmonic sounds within real sound mixtures for automatic music transcription using Independent Subspace Analysis (ISA). The algorithm is based on the assumption that tones played by an instrument within polyphonic music consist of components that are statistically independent from components of other tones. The first step of the algorithm is a temporal segmentation into note events. Both features in the time domain and in the frequency domain are used to detect segment boundaries, which are represented by starting or decaying tones. Each segment is now examined using the ISA and a set of statistically independent components is calculated. One tone played by an instrument consists of the fundamental frequency and its harmonics. Usually, the ISA results in more independent components than played notes, because not all harmonics are separated to the component containing their fundamental frequencies. Some harmonics are separated in components of its own. Using the Kullback-Leibler divergence components belonging together are grouped. A note classification, which is trained for piano music at the time, is the last step of the algorithm. Results show, that statistic independence is a promising measure for separating sounds into single notes using ISA as a step towards automatic music transcription.

Paper Details

Date Published: 17 January 2005
PDF: 7 pages
Proc. SPIE 5682, Storage and Retrieval Methods and Applications for Multimedia 2005, (17 January 2005); doi: 10.1117/12.587164
Show Author Affiliations
Jens Wellhausen, RWTH Aachen Univ. (Germany)
Michael Hoynck, RWTH Aachen Univ. (Germany)

Published in SPIE Proceedings Vol. 5682:
Storage and Retrieval Methods and Applications for Multimedia 2005
Rainer W. Lienhart; Noboru Babaguchi; Edward Y. Chang, Editor(s)

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