K. Kashino, K. Nakadai, T. Kinoshita and H. Tanaka, “Organization of Hierarchical Perceptual Sounds: Music Scene Analysis with Autonomous Processing Modules and a Quantitive Information Integration Mechanism,” Proceedings of International Joint Conferences on Artificial Intelligence, Vol. 1, 1995, pp. 158-164.

has been cited by the following article:

• TITLE: Mixed Music Analysis with Extended Specmurt

  AUTHORS: Daiki Nishimura, Toru Nakashika, Tetsuya Takiguchi, Yasuo Ariki

  KEYWORDS: Mixed Music Analysis; Specmurt Analysis; Matrix Representation

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.6 No.5, May 24, 2013

  ABSTRACT: This paper introduces a mixed music analysis method using extended specmurt analysis. Conventional specmurt can only analyze a multi-pitch music signal from a single instrument and cannot analyze a mixed music signal that has several different types of instruments being played at the same time. To analyze a mixed music signal, extended specmurt is proposed. We regard the observed spectrum extracted from the mixed music as the summation of the observed spectra corresponding to each instrument. The mixed music has as many unknown fundamental frequency distributions as the number of instruments since the observed spectrum of a single instrument can be expressed as a convolution of the common harmonic structure and the fundamental frequency distribution. The relation among the observed spectrum, the common harmonic structure and the fundamental frequency distribution is transformed into a matrix representation in order to obtain the unknown fundamental frequency distributions. The equation is called extended specmurt, and the matrix of unknown components can be obtained by using a pseudo inverse matrix. The experimental result shows the effectiveness of the proposed method.