Instrument Identification in Polyphonic Music: Feature Weighting to Minimize Influence of Sound Overlaps
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Research Article Instrument Identification in Polyphonic Music: Feature Weighting to Minimize Influence of Sound Overlaps Tetsuro Kitahara,1 Masataka Goto,2 Kazunori Komatani,1 Tetsuya Ogata,1 and Hiroshi G. Okuno1 1 Department
of Intelligence Science and Technology, Graduate School of Informatics, Kyoto University, Sakyo-Ku, Kyoto 606-8501, Japan 2 National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki 305-8568, Japan Received 7 December 2005; Revised 27 July 2006; Accepted 13 August 2006 Recommended by Ichiro Fujinaga We provide a new solution to the problem of feature variations caused by the overlapping of sounds in instrument identification in polyphonic music. When multiple instruments simultaneously play, partials (harmonic components) of their sounds overlap and interfere, which makes the acoustic features different from those of monophonic sounds. To cope with this, we weight features based on how much they are affected by overlapping. First, we quantitatively evaluate the influence of overlapping on each feature as the ratio of the within-class variance to the between-class variance in the distribution of training data obtained from polyphonic sounds. Then, we generate feature axes using a weighted mixture that minimizes the influence via linear discriminant analysis. In addition, we improve instrument identification using musical context. Experimental results showed that the recognition rates using both feature weighting and musical context were 84.1% for duo, 77.6% for trio, and 72.3% for quartet; those without using either were 53.4, 49.6, and 46.5%, respectively. Copyright © 2007 Tetsuro Kitahara et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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INTRODUCTION
While the recent worldwide popularization of online music distribution services and portable digital music players has enabled us to access a tremendous number of musical excerpts, we do not yet have easy and efficient ways to find those that we want. To solve this problem, efficient music information retrieval (MIR) technologies are indispensable. In particular, automatic description of musical content in a universal framework is expected to become one of the most important technologies for sophisticated MIR. In fact, frameworks such as MusicXML [1], WEDELMUSIC Format [2], and MPEG-7 [3] have been proposed for describing music or multimedia content. One reasonable approach for this music description is to transcribe audio signals to traditional music scores because the music score is the most common symbolic music representation. Many researchers, therefore, have tried automatic music transcription [4–9], and their techniques can be applied to music description in a score-based format such as MusicXML. However, only a few of them have dealt with identifying musical instruments. Which instruments are
used is important information for two reas
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