scholarly journals An Efficient Audio Classification Approach Based on Support Vector Machines

2016 ◽  
Vol 7 (5) ◽  
Author(s):  
Lhoucine Bahatti ◽  
Omar Bouattane ◽  
My Elhoussine ◽  
Mohamed Hicham
Keyword(s):  
Support Vector Machines ◽  
Support Vector ◽  
Audio Classification ◽  
Classification Approach ◽  
Vector Machines

scholarly journals Optimized Audio Classification and Segmentation Algorithm by Using Ensemble Methods

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Saadia Zahid ◽  
Fawad Hussain ◽  
Muhammad Rashid ◽  
Muhammad Haroon Yousaf ◽  
Hafiz Adnan Habib
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Support Vector Machines ◽  
Real Time ◽  
Ensemble Methods ◽  
Segmentation Algorithm ◽  
Misclassification Rate ◽  
Support Vector ◽  
Audio Classification ◽  
Vector Machines

Audio segmentation is a basis for multimedia content analysis which is the most important and widely used application nowadays. An optimized audio classification and segmentation algorithm is presented in this paper that segments a superimposed audio stream on the basis of its content into four main audio types: pure-speech, music, environment sound, and silence. An algorithm is proposed that preserves important audio content and reduces the misclassification rate without using large amount of training data, which handles noise and is suitable for use for real-time applications. Noise in an audio stream is segmented out as environment sound. A hybrid classification approach is used, bagged support vector machines (SVMs) with artificial neural networks (ANNs). Audio stream is classified, firstly, into speech and nonspeech segment by using bagged support vector machines; nonspeech segment is further classified into music and environment sound by using artificial neural networks and lastly, speech segment is classified into silence and pure-speech segments on the basis of rule-based classifier. Minimum data is used for training classifier; ensemble methods are used for minimizing misclassification rate and approximately 98% accurate segments are obtained. A fast and efficient algorithm is designed that can be used with real-time multimedia applications.

A Novel Dual Parallel Support Vector Machines Algorithm for Audio Classification Based on Genetic Algorithm

2013 ◽  
Vol 19 (3) ◽  
pp. 746-752
Author(s):  
Xiaoqing Yu ◽  
Yunhui Wang ◽  
Wanggen Wan ◽  
Mengyao Zhu ◽  
Huanhuan Liu ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machines ◽  
Support Vector ◽  
Audio Classification ◽  
Vector Machines
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