Open AccessResearch on timbre classification based on BP neural network and MFCC
Author(s) -
Zhihang Meng
Publication year - 2021
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 - 10.1088/1742-6596/1856/1/012006
Subject(s) - timbre , mel frequency cepstrum , artificial neural network , computer science , cepstrum , speech recognition , process (computing) , artificial intelligence , field (mathematics) , pattern recognition (psychology) , feature extraction , musical , mathematics , art , pure mathematics , visual arts , operating system
Music exists in all aspects of life. With the development of deep learning and neural network technology in recent years, music information retrieval has become an emerging field that has received widespread attention. In the past, musicians often relied on manual labeling to classify music. This method is not only time-consuming and labor-intensive, but also inaccurate. Although some researchers have tried to extract music features for automatic classification, it is difficult to extract appropriate audio features due to the interaction between the fundamental wave and harmonics of the music itself. In this paper, the Mel cepstrum coefficient is selected to extract the timbre characteristics, and the improved BP neural network is used to process the characteristics, which overcomes the previous problems and obtains a higher accuracy rate.