Abstract
At the request of the Journal Editor and SAGE Publishing, the following article has been retracted.
Khaleelur Rahiman PF, Jayanthi VS and Jayanthi AN (2020) Speech enhancement method using deep learning approach for hearing-impaired listeners. Health Informatics Journal. Epub ahead of print 23 January 2020. DOI: 10.1177/1460458219893850.
This article contains substantial unreferenced overlap with material from other sources (1-4). An earlier version of this article (4) was previously retracted from Medical & Biological Engineering & Computing and the authors had not disclosed this to the Editor on submission.
In addition, this article contains manipulated data that reduces the validity of the reported findings.
The unattributed excerpts in the article were taken from the following sources:
1. Goehring Tobias, Bolner Federico, Monaghan Jessica JM, van Dijk Bas, Zarowski Andrzej, Bleeck Stefan (2017) Speech enhancement based on neural networks improves speech intelligibility in noise for cochlear implant users. Hearing Research 344: 183-194.
2. Acharya U. Rajendra, Oh Shu Lih, Hagiwara Yuki, Tan, Jen Hong, Adam Muhammad, Gertych Arkadiusz and Tan, Ru San (2017) A Deep Convolutional Neural Network Model to Classify Heartbeats. Computers in Biology and Medicine. 89(1): 389-396.
3. Monaghan Jessica JM, Goehring Tobias and Xin Yang (2017) Auditory inspired machine learning techniques can improve speech intelligibility and quality for hearing-impaired listeners. The Journal of the Acoustical Society of America 141(3). DOI: 10.1177/1477370819839620.
4. Khaleelur Rahiman PF, Jayanthi VS and Jayanthi AN (2019) Deep convolutional neural network-based speech enhancement to improve speech intelligibility and quality for hearing-impaired listeners. Medical & Biological Engineering & Computing 57 (4):757-759.