Time-frequency modeling and classification of pathological voices

被引：0
|
作者：
Umapathy, K [1 ]
Krishnan, S [1 ]
Parsa, V [1 ]
Jamieson, D [1 ]
机构：
[1] Ryerson Univ, Dept Elect & Comp Engn, Toronto, ON, Canada
来源：
SECOND JOINT EMBS-BMES CONFERENCE 2002, VOLS 1-3, CONFERENCE PROCEEDINGS: BIOENGINEERING - INTEGRATIVE METHODOLOGIES, NEW TECHNOLOGIES | 2002年
关键词：
pathological voices; time-frequency decomposition; octaves; matching pursuit;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Acoustic measures of vocal function are routinely used for the assessment of disordered voices, and for monitoring patients' progress over the course of therapy. In this paper, speech signals were decomposed using an adaptive time-frequency transform algorithm, and the signal decomposition parameters such as the octave (scale) maximum, octave mean, and frequency ratio were analyzed using statistical pattern analysis method. A classification accuracy of 93.4% was obtained with a database of 212 speech signals (51 normal and 161 pathological cases).
引用
收藏
页码：116 / 117
页数：2
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