MMSE-Optimal Spectral Amplitude Estimation Given the STFT-Phase

Gerkmann, Timo; Krawczyk, Martin

doi:10.1109/lsp.2012.2233470

Cited by 61 publications

(32 citation statements)

References 16 publications

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“…Note that we formulate the harmonic model in the STFT domain to allow for combinations of the proposed phase reconstruction with spectral amplitude estimators, e.g. [11], [12], [14].…”

Section: B Harmonic Model In the Stft Domainmentioning

confidence: 99%

“…dominated only by the closest harmonic component, and we can thus again employ our simplified signal model (9). From (9) it can be seen that the spectral phases, (12) of bands that are dominated by the same harmonic are directly related via the spectral phase of the shifted analysis window . Accordingly, we can infer the spectral phase of a band from its neighbors by accounting for the phase shift introduced by the spectral representation of the analysis window .…”

Section: Phase Reconstruction Along Frequencymentioning

confidence: 99%

“…Informal listening confirms the noise reduction during voiced speech at the expense of a slightly synthetic sounding residual signal. These artifacts are however effectively alleviated by incorporating uncertainty about the phase estimate and by combination with amplitude enhancement [12]- [14]. This paper is organized as follows: In Section II, we introduce the signal model and derive a novel, visually more informative representation of the spectral phase.…”

Section: Introductionmentioning

confidence: 99%

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STFT Phase Reconstruction in Voiced Speech for an Improved Single-Channel Speech Enhancement

Krawczyk

Gerkmann

2014

IEEE/ACM Trans. Audio Speech Lang. Process.

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155

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The enhancement of speech which is corrupted by noise is commonly performed in the short-time discrete Fourier transform domain. In case only a single microphone signal is available, typically only the spectral amplitude is modified. However, it has recently been shown that an improved spectral phase can as well be utilized for speech enhancement, e.g., for phase-sensitive amplitude estimation. In this paper, we therefore present a method to reconstruct the spectral phase of voiced speech from only the fundamental frequency and the noisy observation. The importance of the spectral phase is highlighted and we elaborate on the reason why noise reduction can be achieved by modifications of the spectral phase. We show 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.Index Terms-Noise reduction, phase estimation, signal reconstruction, speech enhancement.2329-9290 . His main research interests are digital speech and audio processing, including speech enhancement, modeling of speech signals, and hearing devices.

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“…Note that we formulate the harmonic model in the STFT domain to allow for combinations of the proposed phase reconstruction with spectral amplitude estimators, e.g. [11], [12], [14].…”

Section: B Harmonic Model In the Stft Domainmentioning

confidence: 99%

Section: Phase Reconstruction Along Frequencymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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STFT Phase Reconstruction in Voiced Speech for an Improved Single-Channel Speech Enhancement

Krawczyk

Gerkmann

2014

IEEE/ACM Trans. Audio Speech Lang. Process.

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186

155

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“…We demonstrated that the harmonic structure in the clean speech phase spectrum can be recovered by unwrapping the noisy instantaneous phase spectrum followed by threshold-based Here we only focused on evaluating the effectiveness of the pro posed phase estimation method in signal reconstruction stage. The proposed phase estimation can be the basis for recent phase-aware spectral amplitude estimators proposed in [9,10,32] or furthermore to improve robust automatic speech recognition performance.…”

Section: Resultsmentioning

confidence: 99%

A probabilistic approach for phase estimation in single-channel speech enhancement using von mises phase priors

Kulmer

Mowlaee

Watanabe

2014

2014 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)

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In many artificial intelligence systems human voice is considered as the medium for information transmission. Human-machine commu nication by voice becomes difficult when speech is mixed with some background noise. As a remedy, a single-channel speech enhance ment is indispensable for reducing background noise from noisy speech to make it suitable for automatic speech recognition and tele phony speech. While the conventional techniques for single-channel speech enhancement incorporate noisy phase in both amplitude esti mation and signal reconstruction stages, in this paper we propose a probabilistic method to estimate the clean speech phase from noisy observation. Our proposed method consists of phase unwrapping followed by threshold-based temporal smoothing using von Mises phase priors. The proposed phase enhancement method leads to im proved speech quality and intelligibility predicted by instrumental measures without explicit incorporation of amplitude enhancement.

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“…The work of Rahim Saeidi was funded by the European Community's Seventh Framework Programme (FP7 2007-2013 under grant agreement no. 238803.…”

Section: Introductionmentioning

confidence: 99%

On phase importance in parameter estimation in single-channel speech enhancement

Mowlaee

Saeidi

2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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In this paper, we study the impact of exploiting the spectral phase information to further improve the speech quality of the single-channel speech enhancement algorithms. In particular, we focus on the two required steps in a typical single-channel speech enhancement system, namely: parameter estimation solved by a minimum mean square error (MMSE) estimator of the speech spectral amplitude, followed by signal reconstruction stage, where the observed noisy phase is often used. For the parameter estimation stage, in contrast to conventional Wiener filter, a new MMSE estimator is derived which takes into account the clean phase information as a prior information. In our experiments, we show that by including the phase information in the two steps, it is possible to improve the perceived signal quality of the enhanced signal significantly with respect to the methods that do not employ the phase information.Index Terms-Single-channel speech enhancement, phase prior, Wiener filter.

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MMSE-Optimal Spectral Amplitude Estimation Given the STFT-Phase

Cited by 61 publications

References 16 publications

STFT Phase Reconstruction in Voiced Speech for an Improved Single-Channel Speech Enhancement

STFT Phase Reconstruction in Voiced Speech for an Improved Single-Channel Speech Enhancement

A probabilistic approach for phase estimation in single-channel speech enhancement using von mises phase priors

On phase importance in parameter estimation in single-channel speech enhancement

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