Sound Isolation by Harmonic Peak Partition For Music Instrument Recognition

Issue title: Special issue ISMIS'05

Article type: Research Article

Authors: Zhang, Xin | Raś, Zbigniew W.

Affiliations: Department of Computer Science, University of North Carolina, Charlotte NC 28223, USA. E-mail: xinzhang@uncc.edu;ras@uncc.edu

Abstract: Identification of music instruments in polyphonic sounds is difficult and challenging, especially where heterogeneous harmonic partials are overlapping with each other. This has stimulated the research on sound separation for content-based automatic music information retrieval. Numerous successful approaches on musical data feature extraction and selection have been proposed for instrument recognition in monophonic sounds. Unfortunately, none of those algorithms can be successfully applied to polyphonic sounds. Based on recent research in sound classification of monophonic sounds and studies in speech recognition, Moving Picture Experts Group (MPEG) standardized a set of features of the digital audio content data for the purpose of interpretation of the informationmeaning. Most of themare in a formof largematrix or vector of large size, which are not suitable for traditional data mining algorithms; while other features in smaller size are not sufficient for instrument recognition in polyphonic sounds. Therefore, these acoustical features themselves alone cannot be successfully applied to classification of polyphonic sounds. However, these features contain critical information, which implies music instruments' signatures. We have proposed a novel music information retrieval system with MPEG-7-based descriptors and we built classifiers which can retrieve the important time-frequency timbre information and isolate sound sources in polyphonic musical objects, where two instruments are playing at the same time, by energy clustering between heterogeneous harmonic peaks.

Keywords: Music InstrumentsDetection, MPEG-7 descriptors, Musical Sound Separation, Energy Clustering, Sound Classification, and Feature Extraction

Journal: Fundamenta Informaticae, vol. 78, no. 4, pp. 613-628, 2007