Proceedings ArticleDOI
Noise reduction using a soft-decision sine-wave vector quantizer
Thomas F. Quatieri,R.J. McAulay +1 more
- pp 821-824
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TLDR
The results, although preliminary, provide evidence that harmonic zero-phase sine-wave analysis/synthesis, combined with effective estimation of sin-wave amplitudes and probability of voicing, offers a promising approach to noise reduction.Abstract:
Noise reduction is performed in the context of a high-quality harmonic zero-phase sine-wave analysis/synthesis system which is characterized by sine-wave amplitudes, a voicing probability, and a fundamental frequency. Least-squared error estimation of a harmonic sine-wave representation leads to a soft decision template estimate consisting of sine-wave amplitudes and a voicing probability. The least-squares solution is modified to use template-matching with nearest neighbors. The reconstruction is improved by using the modified least-squares solution only in spectral regions with low signal-to-noise ratio. The results, although preliminary, provide evidence that harmonic zero-phase sine-wave analysis/synthesis, combined with effective estimation of sine-wave amplitudes and probability of voicing, offers a promising approach to noise reduction. >read more
Citations
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Journal ArticleDOI
A signal subspace approach for speech enhancement
Yariv Ephraim,H.L. Van Trees +1 more
TL;DR: The popular spectral subtraction speech enhancement approach is shown to be a signal subspace approach which is optimal in an asymptotic (large sample) linear minimum mean square error sense, assuming the signal and noise are stationary.
Journal ArticleDOI
Statistical-model-based speech enhancement systems
TL;DR: A unified statistical approach for the three basic problems of speech enhancement is developed, using composite source models for the signal and noise and a fairly large set of distortion measures.
A Bayesian Estimation Approach for Speech Enhancement Using Hidden
TL;DR: In this paper, a Bayesian estimation approach for enhancing speech signals which have been degraded by statistically independent additive noise is motivated and developed, in particular, minimum mean square error (MMSE) and maximum a posteriori (MAP) signal estimators are developed using hidden Markov models (HMMs) for the clean signal and the noise process.
Journal ArticleDOI
A Bayesian estimation approach for speech enhancement using hidden Markov models
TL;DR: A Bayesian estimation approach for enhancing speech signals which have been degraded by statistically independent additive noise is motivated and developed, and minimum mean square error (MMSE) and maximum a posteriori (MAP) signal estimators are developed using hidden Markov models for the clean signal and the noise process.
Journal ArticleDOI
STFT phase reconstruction in voiced speech for an improved single-channel speech enhancement
Martin Krawczyk,Timo Gerkmann +1 more
TL;DR: It is shown that, when the noisy phase is enhanced using the proposed phase reconstruction, instrumental measures predict an increase of speech quality over a range of signal to noise ratios, even without explicit amplitude enhancement.
References
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Journal ArticleDOI
Speech analysis/Synthesis based on a sinusoidal representation
R.J. McAulay,Thomas F. Quatieri +1 more
TL;DR: A sinusoidal model for the speech waveform is used to develop a new analysis/synthesis technique that is characterized by the amplitudes, frequencies, and phases of the component sine waves, which forms the basis for new approaches to the problems of speech transformations including time-scale and pitch-scale modification, and midrate speech coding.
Journal ArticleDOI
Speech enhancement using a soft-decision noise suppression filter
R. McAulay,M. Malpass +1 more
TL;DR: In this paper, a spectral decomposition of a frame of noisy speech is used to attenuate a particular spectral line depending on how much the measured speech plus noise power exceeds an estimate of the background noise.
Journal ArticleDOI
Speech coding based upon vector quantization
TL;DR: The vector quantizing approach is shown to be a mathematically and computationally tractable method which builds upon knowledge obtained in linear prediction analysis studies and is introduced in a nonrigorous form.
Journal ArticleDOI
Speech transformations based on a sinusoidal representation
Thomas F. Quatieri,R. J. McAulay +1 more
TL;DR: In this paper, a speech analysis/synthesis technique is presented which provides the basis for a general class of speech transformations including time-scale modification, frequency scaling, and pitch modification.
Journal ArticleDOI
On the application of hidden Markov models for enhancing noisy speech
TL;DR: A maximum-a-posteriori approach for enhancing speech signals which have been degraded by statistically independent additive noise is proposed, based on statistical modeling of the clean speech signal and the noise process using long training sequences from the two processes.