Open AccessApplication of Collaborative Representation in Audio Signal Recognition
Author(s) -
Qun.Feng. Huang,
Xiao.Jiang. Li
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 - 10.1088/1742-6596/1827/1/012156
Subject(s) - computer science , classifier (uml) , mel frequency cepstrum , feature extraction , pattern recognition (psychology) , speech recognition , representation (politics) , audio signal , artificial intelligence , scheme (mathematics) , mathematics , speech coding , mathematical analysis , politics , political science , law
Feature extraction and classifier design are the main problems of acoustic signal recognition algorithms. In this paper, we extract the mel-frequency cepstral coefficients of the acoustic features of vehicles in complex scenes. Collaborative representation is introduced for the design of a classification scheme (Collaborative Representative Classification, CRC), which synthetically considers the relationship among samples. Experiments show that the proposed algorithm produces good performance in vehicle recognition for the case of a complex data set. Compared with other classification algorithms, the method improves the precision of recognition.