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

AI-Maathidi, M and Li, FF 2016, Audio content feature selection and classification : a random forests and decision tree approach , in: IEEE International Conference on Progress in Informatics and Computing (PIC), 18-20 December 2015, Nanjing, China.

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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.

Item Type: Conference or Workshop Item (Paper)
Schools: Schools > School of Computing, Science and Engineering
Journal or Publication Title: Progress in Informatics and Computing (PIC), 2015 IEEE International Conference on
Publisher: IEEE
Related URLs:
Depositing User: MM Abd
Date Deposited: 29 Aug 2017 12:03
Last Modified: 29 Aug 2017 12:13
URI: http://usir.salford.ac.uk/id/eprint/43631

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