Information Retrieval Method of Professional Music Teaching Based on Hidden Markov Model

To facilitate professional users to find multimedia files composed of music information more quickly and realize audio frequency content information retrieval, this paper proposes a solution of automatic classification using HMM. Considering the traditional timbre features, the preprocessing process...

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Bibliographic Details
Published inInternational Conference on Measuring Technology and Mechatronics Automation (Print) pp. 1072 - 1075
Main Author Li, Xiumin
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.01.2022
Subjects
Automation
classifier
Feature extraction
Hidden Markov models
HMM
Information retrieval
Mechatronics
music teaching
Simulation
speech recognition
Training
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Summary:To facilitate professional users to find multimedia files composed of music information more quickly and realize audio frequency content information retrieval, this paper proposes a solution of automatic classification using HMM. Considering the traditional timbre features, the preprocessing process of speech signal is analyzed, and the common feature parameter extraction methods are compared to denoise audio information from the aspects of structure and state number. Then HMM model based on notes is used for training and recognition to realize the feature extraction of teaching audio/video files. The simulation results show that the audio classification performance of the hidden Markov model proposed in this paper has better performance, and the optimal classification accuracy is more than 90%.
ISSN:2157-1481
DOI:10.1109/ICMTMA54903.2022.00216