Deep neural network ensemble for reducing artificial noise in bandwidth extension
- Authors
- Noh, Kyoungjin; Chang, Joon Hyuk
- Issue Date
- Jul-2020
- Publisher
- ACADEMIC PRESS INC ELSEVIER SCIENCE
- Keywords
- Bandwidth extension; Deep neural network; Ensemble; Artificial noise
- Citation
- DIGITAL SIGNAL PROCESSING, v.102, pp.1 - 6
- Indexed
- SCIE
SCOPUS
- Journal Title
- DIGITAL SIGNAL PROCESSING
- Volume
- 102
- Start Page
- 1
- End Page
- 6
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/2036
- DOI
- 10.1016/j.dsp.2020.102760
- ISSN
- 1051-2004
- 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.
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