11,665 research outputs found
CMB map restoration
Estimating the cosmological microwave background is of utmost importance for
cosmology. However, its estimation from full-sky surveys such as WMAP or more
recently Planck is challenging: CMB maps are generally estimated via the
application of some source separation techniques which never prevent the final
map from being contaminated with noise and foreground residuals. These spurious
contaminations whether noise or foreground residuals are well-known to be a
plague for most cosmologically relevant tests or evaluations; this includes CMB
lensing reconstruction or non-Gaussian signatures search. Noise reduction is
generally performed by applying a simple Wiener filter in spherical harmonics;
however this does not account for the non-stationarity of the noise. Foreground
contamination is usually tackled by masking the most intense residuals detected
in the map, which makes CMB evaluation harder to perform. In this paper, we
introduce a novel noise reduction framework coined LIW-Filtering for Linear
Iterative Wavelet Filtering which is able to account for the noise spatial
variability thanks to a wavelet-based modeling while keeping the highly desired
linearity of the Wiener filter. We further show that the same filtering
technique can effectively perform foreground contamination reduction thus
providing a globally cleaner CMB map. Numerical results on simulated but
realistic Planck data are provided
Blind Deconvolution of Ultrasonic Signals Using High-Order Spectral Analysis and Wavelets
Defect detection by ultrasonic method is limited by the pulse width.
Resolution can be improved through a deconvolution process with a priori
information of the pulse or by its estimation. In this paper a regularization
of the Wiener filter using wavelet shrinkage is presented for the estimation of
the reflectivity function. The final result shows an improved signal to noise
ratio with better axial resolution.Comment: 8 pages, CIARP 2005, LNCS 377
Rank-1 Constrained Multichannel Wiener Filter for Speech Recognition in Noisy Environments
Multichannel linear filters, such as the Multichannel Wiener Filter (MWF) and
the Generalized Eigenvalue (GEV) beamformer are popular signal processing
techniques which can improve speech recognition performance. In this paper, we
present an experimental study on these linear filters in a specific speech
recognition task, namely the CHiME-4 challenge, which features real recordings
in multiple noisy environments. Specifically, the rank-1 MWF is employed for
noise reduction and a new constant residual noise power constraint is derived
which enhances the recognition performance. To fulfill the underlying rank-1
assumption, the speech covariance matrix is reconstructed based on eigenvectors
or generalized eigenvectors. Then the rank-1 constrained MWF is evaluated with
alternative multichannel linear filters under the same framework, which
involves a Bidirectional Long Short-Term Memory (BLSTM) network for mask
estimation. The proposed filter outperforms alternative ones, leading to a 40%
relative Word Error Rate (WER) reduction compared with the baseline Weighted
Delay and Sum (WDAS) beamformer on the real test set, and a 15% relative WER
reduction compared with the GEV-BAN method. The results also suggest that the
speech recognition accuracy correlates more with the Mel-frequency cepstral
coefficients (MFCC) feature variance than with the noise reduction or the
speech distortion level.Comment: for Computer Speech and Languag
Kajian motivasi ekstrinsik di antara Pelajar Lepasan Sijil dan Diploma Politeknik Jabatan Kejuruteraan Awam KUiTTHO
Kajian ini dijalankan untuk menyelidiki pengaruh dorongan keluarga, cara pengajaran pensyarah, pengaruh rakan sebaya dan kemudahan infrastruktur terhadap motivasi ekstrinsik bagi pelajar tahun tiga dan tahun empat lepasan sijil dan diploma politeknik Jabatan Kejuruteraan Awain Kolej Universiti Teknologi Tun Hussein Onn. Sampel kajian ini beijumlah 87 orang bagi pelajar lepasan sijil politeknik dan 38 orang bagi lepasan diploma politeknik. Data kajian telah diperolehi melalui borang soal selidik dan telah dianalisis menggunakan perisian SPSS (Statical Package For Sciences). Hasil kajian telah dipersembahkan dalam bentuk jadual dan histohgrapi. Analisis kajian mendapati bahawa kedua-dua kumpulan setuju bahawa faktor-faktor di atas memberi kesan kepada motivasi ekstrinsik mereka. Dengan kata lain faktpr-faktor tersebut penting dalam membentuk pelajar mencapai kecemerlangan akademik
Stochastic Wiener Filter in the White Noise Space
In this paper we introduce a new approach to the study of filtering theory by
allowing the system's parameters to have a random character. We use Hida's
white noise space theory to give an alternative characterization and a proper
generalization to the Wiener filter over a suitable space of stochastic
distributions introduced by Kondratiev. The main idea throughout this paper is
to use the nuclearity of this spaces in order to view the random variables as
bounded multiplication operators (with respect to the Wick product) between
Hilbert spaces of stochastic distributions. This allows us to use operator
theory tools and properties of Wiener algebras over Banach spaces to proceed
and characterize the Wiener filter equations under the underlying randomness
assumptions
DNN-Based Multi-Frame MVDR Filtering for Single-Microphone Speech Enhancement
Multi-frame approaches for single-microphone speech enhancement, e.g., the
multi-frame minimum-variance-distortionless-response (MVDR) filter, are able to
exploit speech correlations across neighboring time frames. In contrast to
single-frame approaches such as the Wiener gain, it has been shown that
multi-frame approaches achieve a substantial noise reduction with hardly any
speech distortion, provided that an accurate estimate of the correlation
matrices and especially the speech interframe correlation vector is available.
Typical estimation procedures of the correlation matrices and the speech
interframe correlation (IFC) vector require an estimate of the speech presence
probability (SPP) in each time-frequency bin. In this paper, we propose to use
a bi-directional long short-term memory deep neural network (DNN) to estimate a
speech mask and a noise mask for each time-frequency bin, using which two
different SPP estimates are derived. Aiming at achieving a robust performance,
the DNN is trained for various noise types and signal-to-noise ratios.
Experimental results show that the multi-frame MVDR in combination with the
proposed data-driven SPP estimator yields an increased speech quality compared
to a state-of-the-art model-based estimator
Nonlinear denoising of transient signals with application to event related potentials
We present a new wavelet based method for the denoising of {\it event related
potentials} ERPs), employing techniques recently developed for the paradigm of
deterministic chaotic systems. The denoising scheme has been constructed to be
appropriate for short and transient time sequences using circular state space
embedding. Its effectiveness was successfully tested on simulated signals as
well as on ERPs recorded from within a human brain. The method enables the
study of individual ERPs against strong ongoing brain electrical activity.Comment: 16 pages, Postscript, 6 figures, Physica D in pres
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