Adaptive short-time analysis-synthesis for speech enhancement

Rudoy, Daniel; Basu, Prabahan; Quatieri, Thomas F.; Dunn, Benjamin; Wolfe, Patrick J.

doi:10.1109/icassp.2008.4518757

Cited by 17 publications

(21 citation statements)

References 3 publications

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“…We now apply the enhancement scheme to a synthetic test signal from [5], as well as to a short segment of clarinet music. Both signals were corrupted by additive white Gaussian noise to yield a signalto-noise (SNR) ratio of 5 dB.…”

Section: Enhancement Example Resultsmentioning

confidence: 99%

“…Example of how a fixed-resolution scheme using windows of length L (top) is modified to achieve an adaptive-resolution scheme by merging neighboring windows (bottom). See [5] for details.…”

Section: Wold Representation: General Casementioning

confidence: 98%

“…Audio noise reduction is generally achieved through the attenuation of spectral coefficients using uniformly sized windows and local Fourier analysis. Instead, we propose an adaptive enhancement scheme in which the adaptive analysis scheme of [5] is modulated by the hypothesis test of Section 2. Figure 3 illustrates the adaptive STFT scheme of [5], in which consecutive short-time analysis windows are to be merged based on a measure of time-frequency concentration.…”

Section: Example Applicationmentioning

confidence: 99%

“…Figure 3 illustrates the adaptive STFT scheme of [5], in which consecutive short-time analysis windows are to be merged based on a measure of time-frequency concentration. Here, we evaluate the performance of the proposed hypothesis test of (1) by using it in place of time-frequency concentration as the decision device in this adaptive scheme; the reader is referred to [5] for more details.…”

Section: Example Applicationmentioning

confidence: 99%

“…Waveform reconstruction is achieved by overlap-add synthesis described in [5], as the adaptive analysis scheme still obeys the requisite partition of unity property. …”

Section: Enhancement Systemmentioning

confidence: 99%

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A nonparametric test for stationarity based on local Fourier analysis

Basu

Rudoy

Wolfe

2009

2009 IEEE International Conference on Acoustics, Speech and Signal Processing

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In this paper we propose a nonparametric hypothesis test for stationarity based on local Fourier analysis. We employ a test statistic that measures the variation of time-localized estimates of the power spectral density of an observed random process. For the case of a white Gaussian noise process, we characterize the asymptotic distribution of this statistic under the null hypothesis of stationarity, and use it to directly set test thresholds corresponding to constant false alarm rates. For other cases, we introduce a simple procedure to simulate from the null distribution of interest. After validating the procedure on synthetic examples, we demonstrate one potential use for the test as a method of obtaining a signal-adaptive means of local Fourier analysis and corresponding signal enhancement scheme.

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Section: Enhancement Example Resultsmentioning

confidence: 99%

“…Example of how a fixed-resolution scheme using windows of length L (top) is modified to achieve an adaptive-resolution scheme by merging neighboring windows (bottom). See [5] for details.…”

Section: Wold Representation: General Casementioning

confidence: 98%

Section: Example Applicationmentioning

confidence: 99%

Section: Example Applicationmentioning

confidence: 99%

“…Waveform reconstruction is achieved by overlap-add synthesis described in [5], as the adaptive analysis scheme still obeys the requisite partition of unity property. …”

Section: Enhancement Systemmentioning

confidence: 99%

See 3 more Smart Citations

A nonparametric test for stationarity based on local Fourier analysis

Basu

Rudoy

Wolfe

2009

2009 IEEE International Conference on Acoustics, Speech and Signal Processing

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SURE-Fuse WFF: A Multi-Resolution Windowed Fourier Analysis for Interferometric Phase Denoising

2019

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Interferometric phase (InPhase) imaging is an important part of many present-day coherent imaging technologies. Often in such imaging techniques, the acquired images, known as interferograms, suffer from two major degradations: 1) phase wrapping caused by the fact that the sensing mechanism can only measure sinusoidal 2π-periodic functions of the actual phase, and 2) noise introduced by the acquisition process or the system. This work focusses on InPhase denoising which is a fundamental restoration step to many posterior applications of InPhase, namely to phase unwrapping. The presence of sharp fringes that arises from phase wrapping makes InPhase denoising a hard-inverse problem. Motivated by the fact that the InPhase images are often locally sparse in Fourier domain, we propose a multi-resolution windowed Fourier filtering (WFF) analysis that fuses WFF estimates with different resolutions, thus overcoming the WFF fixed resolution limitation. The proposed fusion relies on an unbiased estimate of the mean square error derived using the Stein's lemma adapted to complex-valued signals. This estimate, known as SURE, is minimized using an optimization framework to obtain the fusion weights. Strong experimental evidence, using synthetic and real (InSAR & MRI) data, that the developed algorithm, termed as SURE-fuse WFF, outperforms the best hand-tuned fixed resolution WFF as well as other state-of-the-art InPhase denoising algorithms, is provided.

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