Document Type
Article
Publication Date
10-1-2022
Publication Title
International Journal of Electrical and Computer Engineering
Volume
12
Issue
5
First Page
5511
Keywords
Chromagram, Classification, Deep learning, Dysphonia, Machine learning, Voice pathology
Last Page
5518
Abstract
This paper presents a convolutional neural network (CNN) based non-invasive pathological voice detection algorithm using signal processing approach. The proposed algorithm extracts an acoustic feature, called chromagram, from voice samples and applies this feature to the input of a CNN for classification. The main advantage of chromagram is that it can mimic the way humans perceive pitch in sounds and hence can be considered useful to detect dysphonic voices, as the pitch in the generated sounds varies depending on the pathological conditions. The simulation results show that classification accuracy of 85% can be achieved with the chromagram. A comparison of the performances for the proposed algorithm with those of other related works is also presented.
DOI
10.11591/ijece.v12i5.pp5511-5518
ISSN
20888708
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Islam, Rumana and Tarique, Mohammed. (2022). A novel convolutional neural network based dysphonic voice detection algorithm using chromagram. International Journal of Electrical and Computer Engineering, 12 (5), 5511-5518.
https://scholar.uwindsor.ca/electricalengpub/486