![]() ![]() Proceedings of 3rd Joint meeting of the Acoustical Society of America and Japan, December 1996, Honolulu, Hawaii, USA Martin KD: Automatic transcription of simple polyphonic music. Proceedings of the International Symposium of Musical Acoustics (ISMA '92), 1992, Tokyo, Japan 79–82.īrown GJ, Cooke M: Perceptual grouping of musical sounds: a computational model. Nagatsuka T, Saiwaki N, Katayose H, Inokuchi S: Automatic transcription system for ensemble music. John Wiley & Sons, New York, NY, USA 2002. Manjunath BS, Salembier P, Sikora T: Introduction of MPEG-7. Proceedings of the International Conference of Web Delivering of Music, November 2001, Florence, Italy 79–86. Proceedings of the XML Conference & Exposition, December 2001, Orlando, Fla, USAīellini P, Nesi P: WEDELMUSIC format: an XML music notation format for emerging applications. ![]() Good M: MusicXML: an internet-friendly format for sheet music. ![]() 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. In addition, we improve instrument identification using musical context. Then, we generate feature axes using a weighted mixture that minimizes the influence via linear discriminant analysis. 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. To cope with this, we weight features based on how much they are affected by overlapping. 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. We provide a new solution to the problem of feature variations caused by the overlapping of sounds in instrument identification in polyphonic music. ![]()
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