1,728 research outputs found
Group-Sparse Signal Denoising: Non-Convex Regularization, Convex Optimization
Convex optimization with sparsity-promoting convex regularization is a
standard approach for estimating sparse signals in noise. In order to promote
sparsity more strongly than convex regularization, it is also standard practice
to employ non-convex optimization. In this paper, we take a third approach. We
utilize a non-convex regularization term chosen such that the total cost
function (consisting of data consistency and regularization terms) is convex.
Therefore, sparsity is more strongly promoted than in the standard convex
formulation, but without sacrificing the attractive aspects of convex
optimization (unique minimum, robust algorithms, etc.). We use this idea to
improve the recently developed 'overlapping group shrinkage' (OGS) algorithm
for the denoising of group-sparse signals. The algorithm is applied to the
problem of speech enhancement with favorable results in terms of both SNR and
perceptual quality.Comment: 14 pages, 11 figure
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
Blind MultiChannel Identification and Equalization for Dereverberation and Noise Reduction based on Convolutive Transfer Function
This paper addresses the problems of blind channel identification and
multichannel equalization for speech dereverberation and noise reduction. The
time-domain cross-relation method is not suitable for blind room impulse
response identification, due to the near-common zeros of the long impulse
responses. We extend the cross-relation method to the short-time Fourier
transform (STFT) domain, in which the time-domain impulse responses are
approximately represented by the convolutive transfer functions (CTFs) with
much less coefficients. The CTFs suffer from the common zeros caused by the
oversampled STFT. We propose to identify CTFs based on the STFT with the
oversampled signals and the critical sampled CTFs, which is a good compromise
between the frequency aliasing of the signals and the common zeros problem of
CTFs. In addition, a normalization of the CTFs is proposed to remove the gain
ambiguity across sub-bands. In the STFT domain, the identified CTFs is used for
multichannel equalization, in which the sparsity of speech signals is
exploited. We propose to perform inverse filtering by minimizing the
-norm of the source signal with the relaxed -norm fitting error
between the micophone signals and the convolution of the estimated source
signal and the CTFs used as a constraint. This method is advantageous in that
the noise can be reduced by relaxing the -norm to a tolerance
corresponding to the noise power, and the tolerance can be automatically set.
The experiments confirm the efficiency of the proposed method even under
conditions with high reverberation levels and intense noise.Comment: 13 pages, 5 figures, 5 table
Exploiting Low-dimensional Structures to Enhance DNN Based Acoustic Modeling in Speech Recognition
We propose to model the acoustic space of deep neural network (DNN)
class-conditional posterior probabilities as a union of low-dimensional
subspaces. To that end, the training posteriors are used for dictionary
learning and sparse coding. Sparse representation of the test posteriors using
this dictionary enables projection to the space of training data. Relying on
the fact that the intrinsic dimensions of the posterior subspaces are indeed
very small and the matrix of all posteriors belonging to a class has a very low
rank, we demonstrate how low-dimensional structures enable further enhancement
of the posteriors and rectify the spurious errors due to mismatch conditions.
The enhanced acoustic modeling method leads to improvements in continuous
speech recognition task using hybrid DNN-HMM (hidden Markov model) framework in
both clean and noisy conditions, where upto 15.4% relative reduction in word
error rate (WER) is achieved
- …