Journal article
New Generalized Sidelobe Canceller with Denoising Auto-Encoder for Improved Speech Enhancement
IEICE transactions on fundamentals of electronics, communications and computer sciences, v E100A(12), pp 3038-3040
01 Dec 2017
Abstract
In this paper, a multichannel speech enhancement system which adopts a denoising auto-encoder as part of the beamformer is proposed. The proposed structure of the generalized sidelobe canceller generates enhanced multi-channel signals, instead of merely one channel, to which the following denoising auto-encoder can be applied. Because the beamformer exploits spatial information and compensates for differences in the transfer functions of each channel, the proposed system is expected to resolve the difficulty of modelling relative transfer functions consisting of complex numbers which are hard to model with a denoising auto-encoder. As a result, the modelling capability of the denoising auto-encoder can concentrate on removing the artefacts caused by the beamformer. Unlike conventional beamformers, which combine these artefacts into one channel, they remain separated for each channel in the proposed method. As a result, the denoising auto-encoder can remove the artefacts by referring to other channels. Experimental results prove that the proposed structure is effective for the six-channel data in CHiME, as indicated by improvements in terms of speech enhancement and word error rate in automatic speech recognition.
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Details
- Title
- New Generalized Sidelobe Canceller with Denoising Auto-Encoder for Improved Speech Enhancement
- Creators
- Minkyu Shin - Korea UniversitySeongkyu Mun - Korea UniversityDavid K. Han - Off Naval Res, Arlington, VA 22217 USAHanseok Ko - Korea University
- Publication Details
- IEICE transactions on fundamentals of electronics, communications and computer sciences, v E100A(12), pp 3038-3040
- Publisher
- Ieice-Inst Electronics Information Communications Eng
- Number of pages
- 3
- Grant note
- Asian Office of Aerospace Research and Development
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000423266000054
- Scopus ID
- 2-s2.0-85038211226
- Other Identifier
- 991021931088104721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Hardware & Architecture
- Computer Science, Information Systems
- Engineering, Electrical & Electronic