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Polyphonic Instrument Recognition Using Spectral Clustering
Citation key 1085Martins2007
Author Luis Gustavo Martins and Juan José Burred and George Tzanetakis and Mathieu Lagrange
Title of Book International Conference on Music Information Retrieval (ISMIR 2007)
Year 2007
Address Vienna, Austria
Month sep
Note L.G. Martins: INESC Porto, Portugal G. Tzanetakis, M. Lagrange: University of Victoria, Canada
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%.
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