scholarly journals A ROBUST APPROACH TO CLASSIFY MICROCALCIFICATION IN DIGITAL MAMMOGRAMS USING CONTOURLET TRANSFORM AND SUPPORT VECTOR MACHINE

2013 ◽  
Vol 6 (1) ◽  
pp. 57-68 ◽  
Author(s):  
Jasmine
Keyword(s):  
Support Vector Machine ◽  
Contourlet Transform ◽  
Support Vector ◽  
Robust Approach

Hyper Accuracy Star Location Algorithm Based on Nonsubsampled Contourlet Transform and Mapped Least Squares Support Vector Machine

2013 ◽  
Vol 33 (5) ◽  
pp. 0512001
Author(s):  
刘南南 Liu Nannan ◽  
徐抒岩 Xu Shuyan ◽  
胡君 Hu Jun ◽  
王栋 Wang Dong ◽  
曹小涛 Cao Xiaotao
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Contourlet Transform ◽  
Support Vector ◽  
Nonsubsampled Contourlet Transform ◽  
Location Algorithm

scholarly journals A Two Microphone-Based Approach for Detecting and Identifying Speech Sounds in Hearing Support System

2019 ◽  
Vol 2 (2) ◽  
pp. 74-86
Author(s):  
Andre Sitompul ◽  
Masafumi Nishimura
Keyword(s):  
Support Vector Machine ◽  
Support System ◽  
Hearing Impaired ◽  
Support Vector ◽  
Speech Sounds ◽  
Sound Sources ◽  
Robust Approach ◽  
Input Channel ◽  
Impaired People ◽  
Life Threatening

For people with hearing disabilities, not only would give them difficulties in going through their everyday lives but also sometimes could be life threatening. In this research we proposed a simple, yet robust approach for helping the hearing-impaired people in identifying the important sounds around them by using two microphones as input channel that could be worn around the person’s head as a substitute for their ears. This device then could be used to record the situation of the surroundings, and then the system would estimate the Direction of Arrival (DOA) of the sound sources, then detect and classify them using Support Vector Machine (SVM) into target speech or noise category. As the results, system’s classifier could produce FAR and FRR as low as 2%, in which 274 out of 280 samples were successfully classified as target speeches and 22 from the total of 27 noise samples were successfully classified as noise

A Two Microphone-Based Approach for Detecting and Identifying Speech Sounds in Hearing Support System

2018 ◽  
Vol 2 (2) ◽  
pp. 74-86
Author(s):  
Andre Sitompul ◽  
Masafumi Nishimura
Keyword(s):  
Support Vector Machine ◽  
Support System ◽  
Hearing Impaired ◽  
Support Vector ◽  
Speech Sounds ◽  
Sound Sources ◽  
Robust Approach ◽  
Input Channel ◽  
Impaired People ◽  
Life Threatening

For people with hearing disabilities, not only would give them difficulties in going through their everyday lives but also sometimes could be life threatening. In this research we proposed a simple, yet robust approach for helping the hearing-impaired people in identifying the important sounds around them by using two microphones as input channel that could be worn around the person’s head as a substitute for their ears. This device then could be used to record the situation of the surroundings, and then the system would estimate the Direction of Arrival (DOA) of the sound sources, then detect and classify them using Support Vector Machine (SVM) into target speech or noise category. As the results, system’s classifier could produce FAR and FRR as low as 2%, in which 274 out of 280 samples were successfully classified as target speeches and 22 from the total of 27 noise samples were successfully classified as noise

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