Kim et al., 2022 - Google Patents
iDeepMMSE: An improved deep learning approach to MMSE speech and noise power spectrum estimation for speech enhancement.Kim et al., 2022
View PDF- Document ID
- 2842941832511614015
- Author
- Kim M
- Song H
- Cheong S
- Shin J
- Publication year
- Publication venue
- Interspeech
External Links
Snippet
Deep learning approaches have been successfully applied to single channel speech enhancement exhibiting significant performance improvement. Recently, approaches unifying deep learning techniques into a statistical speech enhancement framework were …
- 238000001228 spectrum 0 title description 4
Classifications
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- G10L21/00—Processing of the speech or voice signal to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
- G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
- G10L21/0208—Noise filtering
- G10L21/0216—Noise filtering characterised by the method used for estimating noise
- G10L2021/02161—Number of inputs available containing the signal or the noise to be suppressed
- G10L2021/02166—Microphone arrays; Beamforming
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- G10L21/00—Processing of the speech or voice signal to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
- G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
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- G10L21/0232—Processing in the frequency domain
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- G10L15/06—Creation of reference templates; Training of speech recognition systems, e.g. adaptation to the characteristics of the speaker's voice
- G10L15/065—Adaptation
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- G10L15/142—Hidden Markov Models [HMMs]
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- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00-G10L21/00
- G10L25/90—Pitch determination of speech signals
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