Paper Info
Title
Support Vector Machines for Improved Voiceband Classification
Abstract
A method for detecting and classifying the presence of various voiceband data signals on the General Switched Telephone Network (GSTN) is presented. The classification vectors are extracted from processing of the speech parameters evolved by a standard speech coding algorithm. A multi-class Support Vector Machine (SVM) approach is implemented to optimise the classification parameters improving the ability of the system to operate under poor signal-to-noise ratio (SNR) conditions. It is shown that the newly proposed classifier improves on previous implementations and is capable of detecting various 'V' series standards at SNRs well below 12dB.
Year
DOI
Venue
2003
10.1007/978-3-540-45224-9_178
LECTURE NOTES IN COMPUTER SCIENCE
Keywords
Field
DocType
support vector machine,signal to noise ratio,speech coding
Telephone network,Speech coding algorithm,Speech coding,Radial basis function,Pattern recognition,Computer science,Support vector machine,Speech recognition,Artificial intelligence,Classifier (linguistics)
Conference
Volume
ISSN
Citations 
2773
0302-9743
0
PageRank 
References 
Authors
0.34
9
1
Name
Order
Citations
PageRank
S. R. Alty1286.44