Novel dynamic center based binary and ternary pattern network using M4 pooling for real world voice recognition. (15th December 2019)

• Record Type: Journal Article
• Authors: Tuncer, Turker; Dogan, Sengul
• Is Part Of: Applied acoustics. Volume 156(2019)
• Page Start: 176
• Page End: 185
• Publication Date: 2019-12-15
• Subjects: Dynamic center based binary ternary -- Pattern network -- M4 pooling -- Voice classification -- Pattern recognition -- Real world audio recognition; Acoustical engineering -- Periodicals; Periodicals; 620.2
• DOI: 10.1016/j.apacoust.2019.06.029
• Languages: English
• ISSNs: 0003-682X

Abstract

Abstract: The signal processing is one the very important research area in the computer sciences and artificial intelligence. Because, audio recognition, voice activity detection, disease diagnosis, brain activity detection and predictions methods are evaluated using signal processing methods. Nowadays, deep methods have been become popular in the signal processing applications. In this article, a novel hybrid feature extraction network by using novel approximations and multiple pooling method. The proposed method uses both binary pattern (BP) and ternary pattern (TP) as feature extractor. In order to extract variable and distinctive features, dynamic center based feature extraction strategy is used. Hence, the proposed feature extraction network is called as dynamic center based binary and ternary pattern network (DC-BTPNet). The proposed DC-BTPNet is consists of 9 layer. Also, a novel multiple pooling method is used in DC-BTPNet. In order to select features, neighborhood component analysis (NCA) is utilized. Finally, the extracted features are forwarded to polynomial kernel support vector machine (SVM). In order to evaluate performance of the proposed method, a novel dataset is created. The proposed DC-BTPNet based multiple learning method achieved 89.0% accuracy rates and it was compared to other state-of-art convolutional networks. Other well-known conventional classifiers are also used for instance linear discriminant analysis (LDA), k nearest neighbor (KNN) and bagged tree (BT) classifiers are used to compare performance of the classifiers. The comparisons and results clearly proved success of the DC-BTPNet. These results demonstrated that the proposed methods can be achieved successful results in larger datasets.