Single-channel speech enhancement using spectral subtraction in the short-time modulation domain
Author(s)
Paliwal, Kuldip
Wojcicki, Kamil
Schwerin, Belinda
Year published
2010
Metadata
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In this paper we investigate the modulation domain as an alternative to the acoustic domain for speech enhancement. More specifically, we wish to determine how competitive the modulation domain is for spectral subtraction as compared to the acoustic domain. For this purpose, we extend the traditional analysis-modification-synthesis framework to include modulation domain processing. We then compensate the noisy modulation spectrum for additive noise distortion by applying the spectral subtraction algorithm in the modulation domain. Using an objective speech quality measure as well as formal subjective listening tests, we show ...
View more >In this paper we investigate the modulation domain as an alternative to the acoustic domain for speech enhancement. More specifically, we wish to determine how competitive the modulation domain is for spectral subtraction as compared to the acoustic domain. For this purpose, we extend the traditional analysis-modification-synthesis framework to include modulation domain processing. We then compensate the noisy modulation spectrum for additive noise distortion by applying the spectral subtraction algorithm in the modulation domain. Using an objective speech quality measure as well as formal subjective listening tests, we show that the proposed method results in improved speech quality. Furthermore, the proposed method achieves better noise suppression than the MMSE method. In this study, the effect of modulation frame duration on speech quality of the proposed enhancement method is also investigated. The results indicate that modulation frame durations of 180-280 ms, provide a good compromise between different types of spectral distortions, namely musical noise and temporal slurring. Thus given a proper selection of modulation frame duration, the proposed modulation spectral subtraction does not suffer from musical noise artifacts typically associated with acoustic spectral subtraction. In order to achieve further improvements in speech quality, we also propose and investigate fusion of modulation spectral subtraction with the MMSE method. The fusion is performed in the short-time spectral domain by combining the magnitude spectra of the above speech enhancement algorithms. Subjective and objective evaluation of the speech enhancement fusion shows consistent speech quality improvements across input SNRs.
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View more >In this paper we investigate the modulation domain as an alternative to the acoustic domain for speech enhancement. More specifically, we wish to determine how competitive the modulation domain is for spectral subtraction as compared to the acoustic domain. For this purpose, we extend the traditional analysis-modification-synthesis framework to include modulation domain processing. We then compensate the noisy modulation spectrum for additive noise distortion by applying the spectral subtraction algorithm in the modulation domain. Using an objective speech quality measure as well as formal subjective listening tests, we show that the proposed method results in improved speech quality. Furthermore, the proposed method achieves better noise suppression than the MMSE method. In this study, the effect of modulation frame duration on speech quality of the proposed enhancement method is also investigated. The results indicate that modulation frame durations of 180-280 ms, provide a good compromise between different types of spectral distortions, namely musical noise and temporal slurring. Thus given a proper selection of modulation frame duration, the proposed modulation spectral subtraction does not suffer from musical noise artifacts typically associated with acoustic spectral subtraction. In order to achieve further improvements in speech quality, we also propose and investigate fusion of modulation spectral subtraction with the MMSE method. The fusion is performed in the short-time spectral domain by combining the magnitude spectra of the above speech enhancement algorithms. Subjective and objective evaluation of the speech enhancement fusion shows consistent speech quality improvements across input SNRs.
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Journal Title
Speech Communication
Volume
52
Issue
5
Subject
Signal processing
Cognitive and computational psychology
Linguistics
Communications engineering
Artificial intelligence