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Noise Power Spectral Density Tracking: A Maximum Likelihood Perspective
IEEE Signal Processing Letters, 2012We propose a new approach for online noise power spectral density (psd) tracking. In this approach, the prior and posterior probabilities of speech absence and also noise statistics are analytically retrieved from a maximum-likelihood-based criterion at every time-frequency slot.
Mehrez Souden +4 more
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Noise Power Spectral Density Estimation Using MaxNSR Blocking Matrix
IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2015In this paper, a multi-microphone noise reduction system based on the generalized sidelobe canceller (GSC) structure is investigated. The system consists of a fixed beamformer providing an enhanced speech reference, a blocking matrix providing a noise reference by suppressing the target speech, and a single-channel spectral post-filter.
Lin Wang, Timo Gerkmann, Simon Doclo
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Suppression of Additive Noise Using a Power Spectral Density MMSE Estimator
IEEE Signal Processing Letters, 2004In this letter, we propose a novel speech enhancement approach, called power spectral density minimum mean-square error (PSD-MMSE) estimation-based speech enhancement, which is implemented in the power spectral domain where stationary stochastic noise can be modeled as the exponential distribution.
Guo-Hong Ding +2 more
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Noise power spectral density in single-strip NiFeCo-Cu GMR sensors
IEEE Transactions on Magnetics, 2002Noise power spectral density measurement were performed on single-strip patterned NiFeCo-Cu giant magnetoresistance (GMR) multilayers with various width ranging from 2 /spl mu/m to 20 /spl mu/m. The measurements were carried out over two frequency ranges from 0.1 Hz to 100 Hz and 10 Hz to 10 kHz.
A.F. Md Nor, E.W. Hill
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Bias compensation methods for minimum statistics noise power spectral density estimation
Signal Processing, 2006The Minimum Statistics noise power spectral density (psd) estimation approach is based on tracking minima of a short term power spectral density (psd) estimate in frequency subbands. Since the short term minimum power is always smaller than (or in trivial cases equal to) the mean power, the minimum noise power estimate is a biased estimate of the mean ...
Rainer Martin
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DeepMMSE: A Deep Learning Approach to MMSE-Based Noise Power Spectral Density Estimation
IEEE/ACM Transactions on Audio Speech and Language Processing, 2020 An accurate noise power spectral density (PSD) tracker is an indispensable component of a single-channel speech enhancement system. Bayesian-motivated minimum mean-square error (MMSE)-based noise PSD estimators have been the most prominent in recent time.
Qiquan Zhang +2 more
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Power Spectral Density Estimation of Radiated Noise With Sparse Spectral Fitting
Global Oceans 2020: Singapore – U.S. Gulf Coast, 2020When measuring the radiated noise from a noncooperative target by a small-aperture vertical array, the location of the noise source is usually unknown, so most measurement methods are ineffective. To solve this problem, we improve the sparse spectral fitting (SpSF) method so that it can be used to estimate the power spectral density (PSD) of radiated ...
Guoqing Jiang +4 more
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Minimum Subspace Noise Tracking for noise Power Spectral Density estimation
2009 IEEE International Conference on Acoustics, Speech and Signal Processing, 2009Speech enhancement is the processing of speech signals in order to improve one or more perceptual aspects. If the statistics of the clean signal and the noise process are explicitly known, enhancement could be ‘optimally’ accomplished (minimizing a distortion measure between the clean and the estimated signals).
Mahdi Triki, Kees Janse
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Model for the power spectral density of quantization noise
IEEE Transactions on Acoustics, Speech, and Signal Processing, 1981A model is given for the spectrum of the noise produced by passing a signal through a uniform quantizer. The model requires knowledge only of the amplitude distribution of the derivative of this signal. The model is compared with experimental results for a sinusoidal input signal and the sum of two sinusoids.
T. Claasen, A. Jongepier
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Estimation of Relative Transfer Function in the Presence of Stationary Noise Based on Segmental Power Spectral Density Matrix Subtraction [PDF]
, 2015 International audienceThis paper addresses the problem of relative transfer function (RTF) estimation in the presence of stationary noise. We propose an RTF identification method based on segmental power spectral density (PSD) matrix subtraction.
Radu Horaud +2 more
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