Open AccessResearch on Intelligent Recognition Method of Music Similar Segments Based on Deep Reinforcement Learning
Author(s) -
Ni Yen Lu
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 - 10.1088/1742-6596/1992/3/032041
Subject(s) - computer science , similarity (geometry) , key (lock) , relevance (law) , identification (biology) , reinforcement learning , construct (python library) , artificial intelligence , frame (networking) , speech recognition , information retrieval , pattern recognition (psychology) , image (mathematics) , telecommunications , botany , computer security , political science , biology , programming language , law
The identification of similar music segments is of great significance for the study of online music search, content relevance, emotional expression and many other aspects. In the overall structure of music, the extraction of key frames, the identification of similar key frames for different types of music, which is to obtain better music emotion data. This paper uses in-depth reinforcement learning algorithms to analyze the music data in detail to construct music similarity Intelligently identify the database and match the obtained music files with the music data in the database to find similar segments. Case analysis shows that this method can effectively analyze music fragments and provide a basis for subsequent music control.