31,739 research outputs found
Multichannel Speech Separation and Enhancement Using the Convolutive Transfer Function
This paper addresses the problem of speech separation and enhancement from
multichannel convolutive and noisy mixtures, \emph{assuming known mixing
filters}. We propose to perform the speech separation and enhancement task in
the short-time Fourier transform domain, using the convolutive transfer
function (CTF) approximation. Compared to time-domain filters, CTF has much
less taps, consequently it has less near-common zeros among channels and less
computational complexity. The work proposes three speech-source recovery
methods, namely: i) the multichannel inverse filtering method, i.e. the
multiple input/output inverse theorem (MINT), is exploited in the CTF domain,
and for the multi-source case, ii) a beamforming-like multichannel inverse
filtering method applying single source MINT and using power minimization,
which is suitable whenever the source CTFs are not all known, and iii) a
constrained Lasso method, where the sources are recovered by minimizing the
-norm to impose their spectral sparsity, with the constraint that the
-norm fitting cost, between the microphone signals and the mixing model
involving the unknown source signals, is less than a tolerance. The noise can
be reduced by setting a tolerance onto the noise power. Experiments under
various acoustic conditions are carried out to evaluate the three proposed
methods. The comparison between them as well as with the baseline methods is
presented.Comment: Submitted to IEEE/ACM Transactions on Audio, Speech and Language
Processin
Differential fast fixed-point algorithms for underdetermined instantaneous and convolutive partial blind source separation
This paper concerns underdetermined linear instantaneous and convolutive
blind source separation (BSS), i.e., the case when the number of observed mixed
signals is lower than the number of sources.We propose partial BSS methods,
which separate supposedly nonstationary sources of interest (while keeping
residual components for the other, supposedly stationary, "noise" sources).
These methods are based on the general differential BSS concept that we
introduced before. In the instantaneous case, the approach proposed in this
paper consists of a differential extension of the FastICA method (which does
not apply to underdetermined mixtures). In the convolutive case, we extend our
recent time-domain fast fixed-point C-FICA algorithm to underdetermined
mixtures. Both proposed approaches thus keep the attractive features of the
FastICA and C-FICA methods. Our approaches are based on differential sphering
processes, followed by the optimization of the differential nonnormalized
kurtosis that we introduce in this paper. Experimental tests show that these
differential algorithms are much more robust to noise sources than the standard
FastICA and C-FICA algorithms.Comment: this paper describes our differential FastICA-like algorithms for
linear instantaneous and convolutive underdetermined mixture
Of `Cocktail Parties' and Exoplanets
The characterisation of ever smaller and fainter extrasolar planets requires
an intricate understanding of one's data and the analysis techniques used.
Correcting the raw data at the 10^-4 level of accuracy in flux is one of the
central challenges. This can be difficult for instruments that do not feature a
calibration plan for such high precision measurements. Here, it is not always
obvious how to de-correlate the data using auxiliary information of the
instrument and it becomes paramount to know how well one can disentangle
instrument systematics from one's data, given nothing but the data itself. We
propose a non-parametric machine learning algorithm, based on the concept of
independent component analysis, to de-convolve the systematic noise and all
non-Gaussian signals from the desired astrophysical signal. Such a `blind'
signal de-mixing is commonly known as the `Cocktail Party problem' in
signal-processing. Given multiple simultaneous observations of the same
exoplanetary eclipse, as in the case of spectrophotometry, we show that we can
often disentangle systematic noise from the original light curve signal without
the use of any complementary information of the instrument. In this paper, we
explore these signal extraction techniques using simulated data and two data
sets observed with the Hubble-NICMOS instrument. Another important application
is the de-correlation of the exoplanetary signal from time-correlated stellar
variability. Using data obtained by the Kepler mission we show that the desired
signal can be de-convolved from the stellar noise using a single time series
spanning several eclipse events. Such non-parametric techniques can provide
important confirmations of the existent parametric corrections reported in the
literature, and their associated results. Additionally they can substantially
improve the precision exoplanetary light curve analysis in the future.Comment: ApJ accepte
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