Phase estimation in speech enhancement &amp;#x2014; Unimportant, important, or impossible?

Gerkmann, Timo; Krawczyk, Martin; Rehr, Robert

doi:10.1109/eeei.2012.6376931

Cited by 19 publications

(18 citation statements)

References 20 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%

“…For all other harmonic frequencies, the phase difference will differ from zero. We can reformulate (10) to get (11) With (11) we can reconstruct the spectral phase of a harmonic signal based on the fundamental frequency and the segment shift , given that we have a phase estimate at a single signal segment , i.e.…”

Section: Phase Reconstruction Along Timementioning

confidence: 99%

“…In these bands we propose to use the noisy phase as an initial estimate of the clean spectral phase at the onset of a voiced sound, . From this initial value the spectral phase of consecutive segments is then reconstructed using (11). It is worth noting that the alignment of phases of harmonic components over consecutive segments has also been discussed in the context of sinusoidal signal analysis and synthesis, e.g.…”

Section: Phase Reconstruction Along Timementioning

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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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 Timementioning

confidence: 99%

Section: Phase Reconstruction Along Timementioning

confidence: 99%

See 1 more Smart Citation

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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“…Though most of the recent publications take the phase information into an account, it is still not clear that whether the phase is important or unimportant. 41 Variety of approaches have been proposed, for example, a procedure that employs speech corpus or inventory-driven resynthesis mechanism and are therefore termed as corpus based approach, 42 and its improved method in Ref. 43, the advantages of this approach is that it includes the spectral phase.…”

Section: Recent Developmentsmentioning

confidence: 99%

A review on speech enhancement algorithms and why to combine with environment classification

Nidhyananthan¹,

Kumari²,

Prakash³

2014

Int. J. Mod. Phys. C

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Speech enhancement has been an intensive research for several decades to enhance the noisy speech that is corrupted by additive noise, multiplicative noise or convolutional noise. Even after decades of research it is still the most challenging problem, because most papers rely on estimating the noise during the nonspeech activity assuming that the background noise is uncorrelated (statistically independent of speech signal), nonstationary and slowly varying, so that the noise characteristics estimated in the absence of speech can be used subsequently in the presence of speech, whereas in a real time environment such assumptions do not hold for all the time. In this paper, we discuss the historical development of approaches that starts from the year 1970 to, the recent, 2013 for enhancing the noisy speech corrupted by additive background noise. Seeing the history, there are algorithms that enhance the noisy speech very well as long as a speci¯c application is concerned such as the In-car noisy environments. It has to be observed that a speech enhancement algorithm performs well with a good estimation of the noise Power Spectral Density (PSD) from the noisy speech. Our idea pops up based on this observation, for online speech enhancement (i.e. in a real time environment) such as mobile phone applications, instead of estimating the noise from the noisy speech alone, the system should be able to monitor an environment continuously and classify it. Based on the current environment of the user, the system should adapt the algorithm (i.e. enhancement or estimation algorithm) for the current environment to enhance the noisy speech.

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“…We know from [10] that phase estimation improves the predicted speech quality and we proposed in [11] a dereverberation method that uses phase information to improve the dereverberation performance. However, this method is restricted to linear chirp signals.…”

Section: Introductionmentioning

confidence: 99%

Amplitude and phase dereverberation of monocomponent signals

Belhomme

Badeau

Grenier

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-While most dereverberation methods focus on how to estimate the amplitude of an anechoic signal, we propose a method which also takes the phase into account. By applying a sinusoidal model to the anechoic signal, we derive a formulation to compute the amplitude and phase of each sinusoid. These parameters are then estimated by our method in the reverberant case. As we jointly estimate the amplitude and phase of the clean signal, we achieve a very strong dereverberation, resulting in a significant improvement of objective dereverberation measures over the state-of-the-art.

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Phase estimation in speech enhancement — Unimportant, important, or impossible?

Cited by 19 publications

References 20 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 review on speech enhancement algorithms and why to combine with environment classification

Amplitude and phase dereverberation of monocomponent signals

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