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Deep neural network ensemble for reducing artificial noise in bandwidth extension

Title
Deep neural network ensemble for reducing artificial noise in bandwidth extension
Author
장준혁
Keywords
Bandwidth extension; Deep neural network; Ensemble; Artificial noise
Issue Date
2020-05
Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
Citation
DIGITAL SIGNAL PROCESSING, v. 102, article no. 102760
Abstract
In this paper, we propose a deep neural network (DNN) ensemble for reducing artificial noise in speech bandwidth extension (BWE). The proposed DNN ensemble consists of three DNN models; one is a classification model, and the other two are regression models. When estimating sub-band energies of the high-frequency region using sequential DNNs in a frequency domain, the over-estimation of sub-band energies causes annoying artificial noise. To mitigate this artificial noise, we design a DNN classification model that can classify over-estimation frames against normal frames. Then, we separately develop two DNN regression models using half of the entire training set and a limited training set built with overestimation frames and some normal frames to improve the performance at the over-estimation frames. Since the outputs of the classification model are probabilities of either a normal frame or an overestimation frame, respectively, two regression models are adjustably combined by using the probabilistic weights; thus, the final output of the DNN ensemble is the weighted sum of two estimated sub-band energies. As a result, artificial noise is significantly reduced, yielding improved speech quality. The proposed method is objectively and subjectively evaluated by comparing it with conventional approaches.
URI
https://www.sciencedirect.com/science/article/pii/S1051200420301056?via%3Dihubhttps://repository.hanyang.ac.kr/handle/20.500.11754/166516
ISSN
1051-2004; 1095-4333
DOI
10.1016/j.dsp.2020.102760
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > ELECTRONIC ENGINEERING(융합전자공학부) > Articles
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