Audio content feature selection and classification : a random forests and decision tree approach

AI-Maathidi, M; Li, FF

Abstract

Content information can be extracted from soundtracks of multimedia files. A good audio classifier as a preprocessor
is crucial in such applications. Efforts have been made
to develop effective and efficient audio content classifiers in
which features were often selected in ad hoc or empirical ways. This paper proposes a set of systematic methods that use the random forests and decision trees to select features and support decisions. The proposed methods allow for heuristic formation of feature spaces, mitigating redundancy in datasets. The performance of the proposed methods has been compared with other common audio classifiers, and improvements in performance have been noted: feature spaces simplified, computational overhead reduced, and classification accuracy improved.

Citation

AI-Maathidi, M., & Li, F. (2015, December). Audio content feature selection and classification : a random forests and decision tree approach. Presented at IEEE International Conference on Progress in Informatics and Computing (PIC), Nanjing, China

• Presentation Conference Type: Other
• Conference Name: IEEE International Conference on Progress in Informatics and Computing (PIC)
• Conference Location: Nanjing, China
• Start Date: Dec 18, 2015
• End Date: Dec 20, 2015
• Publication Date: Jun 13, 2016
• Deposit Date: Aug 29, 2017
• Book Title: 2015 IEEE International Conference on Progress in Informatics and Computing (PIC)
• ISBN: 9781467390880;-9781467380867
• DOI: https://doi.org/10.1109/PIC.2015.7489819
• Publisher URL: http://dx.doi.org/10.1109/PIC.2015.7489819
• Related Public URLs: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7467813
• Additional Information: Event Type : Conference