5,379 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
A General Family of Penalties for Combining Differing Types of Penalties in Generalized Structured Models
Penalized estimation has become an established tool for regularization and model selection in regression models.
A variety of penalties with specific features are available
and effective algorithms for specific penalties have been proposed.
But not much is available to fit models that call for a combination of different penalties.
When modeling rent data, which will be considered as an example, various types of predictors call for a combination of a Ridge, a grouped Lasso and a Lasso-type penalty within one model.
Algorithms that can deal with such problems, are in demand.
We propose to approximate penalties that are (semi-)norms of scalar linear transformations of the coefficient vector in generalized structured models.
The penalty is very general such that the Lasso, the fused Lasso, the Ridge, the smoothly clipped absolute deviation penalty (SCAD), the elastic net and many more penalties are embedded.
The approximation allows to combine all these penalties within one model.
The computation is based on conventional penalized iteratively re-weighted least squares (PIRLS) algorithms and hence, easy to implement.
Moreover, new penalties can be incorporated quickly.
The approach is also extended to penalties with vector based arguments; that is, to penalties with norms of linear transformations of the coefficient vector.
Some illustrative examples and the model for the Munich rent data show promising results
Efficient Learning of Sparse Conditional Random Fields for Supervised Sequence Labelling
Conditional Random Fields (CRFs) constitute a popular and efficient approach
for supervised sequence labelling. CRFs can cope with large description spaces
and can integrate some form of structural dependency between labels. In this
contribution, we address the issue of efficient feature selection for CRFs
based on imposing sparsity through an L1 penalty. We first show how sparsity of
the parameter set can be exploited to significantly speed up training and
labelling. We then introduce coordinate descent parameter update schemes for
CRFs with L1 regularization. We finally provide some empirical comparisons of
the proposed approach with state-of-the-art CRF training strategies. In
particular, it is shown that the proposed approach is able to take profit of
the sparsity to speed up processing and hence potentially handle larger
dimensional models
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