A Comparative Study Of LRC, NNLS, Knn, And Sparse Representation Based Classification Methods Based On Audio Features
Volume 2 - Issue 9, September 2018 Edition
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Author(s)
Nu War
Keywords
MFCC, DWT, SRC, NNLS, LRC, MSRC, and kNN.
Abstract
Video comprises of two signal modes the acoustic and visual. The visual information is potentially more difficult to extract. In this work, only acoustic signal mode are considered and assessed for the task of genre classification. In the video genre classification field, there are many genres in the real world such as music, sport, information, education, news, and so on. In this paper, the three genres are only considered to classify such as cartoon, sport and music. The experiments with them have also provided a reference of the performance of such systems when dealing with the own video data set. Furthermore, it has been experimented with five classification methods (SRC, NNLS, LRC, MSRC, and kNN) in order to improve accuracy and to see relevant aspects and processes of them. It consists of discrete wavelet subband features, then computes the mean and variances for all. Furthermore, MFCC features are also implemented in feature extraction. Finally, a proper evaluation of the solution has been done. The overall accuracy and classification are also shown in the experimental results.
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