1. Classification of Broadcast News Audio Data Employing Binary Decision Architecture
- Author
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Peter Feciľak, Anton Čižmár, Jozef Juhar, Jozef Vavrek, and The research in this paper was supported by the Ministry of Education, Science, Research and Sport of the Slovak Republic under the project VEGA 2/0197/15 and the Slovak Research and Development Agency under the project APVV-15-0517.
- Subjects
0209 industrial biotechnology ,Computational complexity theory ,Computer Networks and Communications ,Computer science ,Binary number ,68T10 ,02 engineering and technology ,computer.software_genre ,Multiclass classification ,020901 industrial engineering & automation ,0202 electrical engineering, electronic engineering, information engineering ,Architecture ,Support vector machine, audio classification, broadcast news data, binary decision trees, binary decision architecture ,other areas of Computing and Informatics ,Binary decision diagram ,business.industry ,020208 electrical & electronic engineering ,Pattern recognition ,Support vector machine ,ComputingMethodologies_PATTERNRECOGNITION ,Computational Theory and Mathematics ,Hardware and Architecture ,Data mining ,Artificial intelligence ,Decision table ,business ,computer ,Classifier (UML) ,Software - Abstract
A novel binary decision architecture (BDA) for broadcast news audio classification task is presented in this paper. The idea of developing such architecture came from the fact that the appropriate combination of multiple binary classifiers for two-class discrimination problem can reduce a miss-classification error without rapid increase in computational complexity. The core element of classification architecture is represented by a binary decision (BD) algorithm that performs discrimination between each pair of acoustic classes, utilizing two types of decision functions. The first one is represented by a simple rule-based approach in which the final decision is made according to the value of selected discrimination parameter. The main advantage of this solution is relatively low processing time needed for classification of all acoustic classes. The cost for that is low classification accuracy. The second one employs support vector machine (SVM) classifier. In this case, the overall classification accuracy is conditioned by finding the optimal parameters for decision function resulting in higher computational complexity and better classification performance. The final form of proposed BDA is created by combining four BD discriminators supplemented by decision table. The effectiveness of proposed BDA, utilizing rule-based approach and the SVM classifier, is compared with two most popular strategies for multiclass classification, namely the binary decision trees (BDT) and the One-Against-One SVM (OAOSVM). Experimental results show that the proposed classification architecture can decrease the overall classification error in comparison with the BDT architecture. On the contrary, an optimization technique for selecting the optimal set of training data is needed in order to overcome the OAOSVM.
- Published
- 2017