@INPROCEEDINGS{1085Martins2007,
	AUTHOR = {Luis Gustavo Martins and Juan José Burred and George Tzanetakis and Mathieu Lagrange},
	TITLE = {Polyphonic Instrument Recognition Using Spectral Clustering},
	BOOKTITLE = {International Conference on Music Information Retrieval (ISMIR 2007)},
	YEAR = {2007},
	MONTH = sep,
	ADDRESS = {Vienna, Austria},
	NOTE = {L.G. Martins: INESC Porto, Portugal
G. Tzanetakis, M. Lagrange: University of Victoria, Canada},
	PDF = {http://elvera.nue.tu-berlin.de/files/1085Martins2007.pdf},
	ABSTRACT = {The identification of the instruments playing in a polyphonic music signal is an important and unsolved problem in Music Information Retrieval. In this paper, we propose a framework for the sound source separation and timbre classification of polyphonic, multi-instrumental music signals. The sound source separation method is inspired by ideas from Computational Auditory Scene Analysis and formulated as a graph partitioning problem. It utilizes a sinusoidal analysis front-end and makes use of the normalized cut, applied as a global criterion for segmenting graphs. Timbre models for six musical instruments are used for the classification of the resulting sound sources. The proposed framework is evaluated on a dataset consisting of mixtures of a variable number of simultaneous pitches and instruments, up to a maximum of four concurrent notes. The overall instrument classification success rate is of 47%.}
}