Open AccessAnalysis and Recognition of Cello Timbre Based on Deep Trust Network Model
Author(s) -
Peng Sun
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1533/2/022015
Subject(s) - tone (literature) , timbre , cello , similarity (geometry) , computer science , set (abstract data type) , speech recognition , artificial intelligence , feature (linguistics) , selection (genetic algorithm) , data set , pattern recognition (psychology) , musical , acoustics , art , linguistics , philosophy , physics , image (mathematics) , piano , visual arts , programming language , literature
Voice color analysis and similarity calculation of music signals are the important research contents of computer music information retrieval system. In this paper, the deep trust network model is applied to the study of musical tone model. The 72-dimensional features of the cello tone are first extracted. Using the wrapper feature selection method, a 14-dimensional optimal feature subset that reflects the tone characteristics is selected, which greatly reduces the complexity of cello tone similarity calculation. In the set, SVR is used to classify and distinguish eight types of tone data, and a recognition accuracy of 62% was achieved, which is verified the feasibility of the tone model.