Research on the improved method of fundamental frequency extraction for music automatic recognition of piano music

Issue title: Special Section: Applications of intelligent & fuzzy theory in engineering technologies and applied science

Guest editors: Stanley Lima and Álvaro Rocha

Article type: Research Article

Authors: Wang, Xiaomeng^{; *}

Correspondence: [*] Corresponding author. Xiaomeng Wang, Conservatory of Music, Qingdao University, Qingdao 266071, China. E-mail: [email protected].

Abstract: Music recognition is an interdisciplinary field, in the field of music retrieval and automatic music has very important application value in technology. In order to study the improvement method of music recognition for piano music, this paper compared the characteristics of music signals and speech signals around music related theories, discussed the selection of dimension of feature vectors, and used RBF neural network to identify 88 monosyllabic pianos. At the same time, the characteristics and calculation methods of the sound level contour with high frequency in western music and chord recognition were studied, and the specific formulas were given. The final study shows that: The improved method gives intermediate weights more inclined note nest, which has a higher accuracy than the traditional method and fault tolerance.

Keywords: Music recognition, RBF neural network, base frequency extraction of music

DOI: 10.3233/JIFS-169630

Journal: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 3, pp. 2777-2783, 2018