Speech Enhancement Based on Data-Driven Residual Gain Estimationopen access
- Authors
- Jin, Yu Gwang; Kim, Nam Soo; Chang, Joon-Hyuk
- Issue Date
- Dec-2011
- Publisher
- IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG
- Keywords
- speech enhancement; noise reduction; data-driven approach; residual gain estimation
- Citation
- IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, v.E94D, no.12, pp.2537 - 2540
- Indexed
- SCOPUS
- Journal Title
- IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS
- Volume
- E94D
- Number
- 12
- Start Page
- 2537
- End Page
- 2540
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/27644
- DOI
- 10.1587/transinf.E94.D.2537
- ISSN
- 1745-1361
- Abstract
- In this letter, we propose a novel speech enhancement algorithm based on data-driven residual gain estimation. The entire system consists of two stages. At the first stage, a conventional speech enhancement algorithm enhances the input signal while estimating several signal-to-noise ratio (SNR)-related parameters. The residual gain, which is estimated by a data-driven method, is applied to further enhance the signal at the second stage. A number of experimental results show that the proposed speech enhancement algorithm outperforms the conventional speech enhancement technique based on soft decision and the data-driven approach using SNR grid look-up table.
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