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ABSTRACT: We propose a denoising and segmentation technique for the second heart sound (S2). To denoise, Matching Pursuit (MP) was applied using a set of non-linear chirp signals as atoms. We show that the proposed method can be used to segment the phonocardiogram of the second heart sound into its two clinically meaningful components: the aortic (A2) and pulmonary (P2) components.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 08/2012; 2012:3440-3.
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ABSTRACT: We propose the audio inpainting framework that recovers portions of audio data distorted due to impairments such as impulsive noise, clipping, and packet loss. In this framework, the distorted data are treated as missing and their location is assumed to be known. The signal is decomposed into overlapping time-domain frames and the restoration problem is then formulated as an inverse problem per audio frame. Sparse representation modeling is employed per frame, and each inverse problem is solved using the Orthogonal Matching Pursuit algorithm together with a discrete cosine or a Gabor dictionary. The Signal-to-Noise Ratio performance of this algorithm is shown to be comparable or better than state-of-the-art methods when blocks of samples of variable durations are missing. We also demonstrate that the size of the block of missing samples, rather than the overall number of missing samples, is a crucial parameter for high quality signal restoration. We further introduce a constrained Matching Pursuit approach for the special case of audio declipping that exploits the sign pattern of clipped audio samples and their maximal absolute value, as well as allowing the user to specify the maximum amplitude of the signal. This approach is shown to outperform state-of-the-art and commercially available methods for audio declipping in terms of Signal-to-Noise Ratio.
IEEE Transactions on Audio Speech and Language Processing 04/2012; · 1.50 Impact Factor
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ABSTRACT: For dictionary-based decompositions of certain types, it has been observed that there might be a link between sparsity in the dictionary and sparsity in the decomposition. Sparsity in the dictionary has also been associated with the derivation of fast and efficient dictionary learning algorithms. Therefore, in this paper we present a greedy adaptive dictionary learning algorithm that sets out to find sparse atoms for speech signals. The algorithm learns the dictionary atoms on data frames taken from a speech signal. It iteratively extracts the data frame with minimum sparsity index, and adds this to the dictionary matrix. The contribution of this atom to the data frames is then removed, and the process is repeated. The algorithm is found to yield a sparse signal decomposition, supporting the hypothesis of a link between sparsity in the decomposition and dictionary. The algorithm is applied to the problem of speech representation and speech denoising, and its performance is compared to other existing methods. The method is shown to find dictionary atoms that are sparser than their time-domain waveform, and also to result in a sparser speech representation. In the presence of noise, the algorithm is found to have similar performance to the well established principal component analysis.
IEEE Journal of Selected Topics in Signal Processing 10/2011; · 2.88 Impact Factor
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ABSTRACT: In this paper, we consider the problem of separating a set of independent components when only one movable sensor is available to record the mixtures. We propose to exploit the quasi-periodicity of the heart signals to transform the signal from this one moving sensor, into a set of measurements, as if from a virtual array of sensors. We then use ICA to perform source separation. We show that this technique can be applied to heart sounds and to electrocardiograms.
Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on; 06/2011 · 4.63 Impact Factor
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ABSTRACT: We present a greedy adaptive algorithm that builds a sparse orthogonal dictionary from the observed data. In this paper, the algorithm is used to separate stereo speech signals, and the phase information that is inherent to the extracted atom pairs is used for clustering and identification of the original sources. The performance of the algorithm is compared to that of the adaptive stereo basis algorithm, when the sources are mixed in echoic and anechoic environments. We find that the algorithm correctly separates the sources, and can do this even with a relatively small number of atoms.
Hands-Free Speech Communication and Microphone Arrays, 2008. HSCMA 2008; 06/2008
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ABSTRACT: In this paper we consider the problem of representing a speech signal with an adaptive transform that captures the main features of the data. The transform is orthogonal by construction, and is found to give a sparse representation of the data being analysed. The orthogonality property implies that evaluation of both the forward and inverse transform involve a simple matrix multiplication. The proposed dictionary learning algorithm is compared to the K singular value decomposition (K-SVD) method, which is found to yield very sparse representations, at the cost of a high approximation error. The proposed algorithm is shown to have a much lower computational complexity than K-SVD, while the resulting signal representation remains relatively sparse.
Communications, Control and Signal Processing, 2008. ISCCSP 2008. 3rd International Symposium on; 04/2008
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ABSTRACT: A sequential algorithm for the blind separation of a class of periodic source signals is introduced in this paper. The algorithm is based only on second-order statistical information and exploits the assumption that the source signals have distinct periods. Separation is performed by sequentially converging to a solution which in effect diagonalizes the output covariance matrix constructed at a lag corresponding to the fundamental period of the source we select, the one with the smallest period. Simulation results for synthetic signals and real electrocardiogram recordings show that the proposed algorithm has the ability to restore statistical independence, and its performance is comparable to that of the equivariant adaptive source separation (EASI) algorithm, a benchmark high-order statistics-based sequential algorithm with similar computational complexity. The proposed algorithm is also shown to mitigate the limitation that the EASI algorithm can separate at most one Gaussian distributed source. Furthermore, the steady-state performance of the proposed algorithm is compared with that of EASI and the block-based second-order blind identification (SOBI) method.
IEEE Transactions on Signal Processing 04/2006; · 2.63 Impact Factor
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ABSTRACT: This work addresses the problem of fetal electrocardiogram extraction using blind source separation (BSS) in the wavelet domain. A new approach is proposed, which is particularly advantageous when the mixing environment is noisy and time-varying, and that is shown, analytically and in simulation, to improve the convergence rate of the natural gradient algorithm. The distribution of the wavelet coefficients of the source signals is then modeled by a generalized Gaussian probability density, whereby in the time-scale domain the problem of selecting appropriate nonlinearities when separating mixtures of both sub- and super-Gaussian signals is mitigated, as shown by experimental results.
IEEE Transactions on Biomedical Engineering 04/2005; · 2.28 Impact Factor
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ABSTRACT: An adaptive algorithm for the blind separation of periodic sources is proposed in this paper. The method uses only the second order statistics of the data, and exploits the periodic nature of the source signals. Simulation results show that the proposed approach has the ability to restore statistical independence, and its performance is comparable to that of a well-established, higher order, blind source separation method.
Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on; 08/2004
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ABSTRACT: A new variable step-size equivariant adaptive source separation via independence (VS-EASI) algorithm for on-line blind separation of independent sources is proposed. The algorithm utilises a variable step-size and thereby matches its performance to the dynamics of the input signals together with changes in the underlying mixing matrix. The algorithm is therefore particularly well suited to blind source separation in a non-stationary environment. Monte Carlo simulation studies support the expected improvement in convergence speed of the approach.
Electronics Letters 04/2004; · 0.96 Impact Factor
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ABSTRACT: A new natural gradient type algorithm (NGA) for the separation of cyclostationary sources is introduced. Based on the interpretation of blind source separation (BSS) as a two-stage process, including prewhitening and rotation, the cyclostationary NGA (CSNGA) algorithm is constructed such that it also ensures that the recovered sources are decorrelated in the cyclostationary sense. The method is generalised to the case of complex valued source signals, and modified so that adequate algorithm performance is attained even when only one source cycle frequency is known. The properties of the new algorithm are investigated when additive white Gaussian noise is present, and it is found that, in general, the CSNGA approach improves the convergence properties of the natural gradient algorithm. Computer simulations support the validity of the approach.
IEE Proceedings - Vision Image and Signal Processing 03/2004;
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ABSTRACT: A fast converging natural gradient algorithm (NGA) for the sequential blind separation of cyclostationary sources is proposed.
The approach employs an adaptive learning rate which changes in response to the changes in the dynamics of the sources. This
way the convergence and the robustness to the initial choice of parameters are much improved over the standard algorithm.
The additional computational complexity of the proposed algorithm is negligible as compared to the cyclostationary NGA method.
Simulations results support the analysis.
10/2003: pages 1343-1349;
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ABSTRACT: An on-line adaptive blind source separation algorithm for the separation of convolutive mixtures of cyclostationary source signals is proposed. The algorithm is derived by applying natural gradient iterative learning to the novel cost function which is defined according to the wide sense cyclostationarity of signals. The efficiency of the algorithm is supported by simulations, which show that the proposed algorithm has improved performance for the separation of convolved cyclostationary signals in terms of convergence speed and waveform similarity measurement, as compared to the conventional natural gradient algorithm for convolutive mixtures.
Signal Processing and Its Applications, 2003. Proceedings. Seventh International Symposium on; 08/2003
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ABSTRACT: A normalised natural gradient algorithm (NGA) for the separation of cyclostationary source signals is proposed in this paper. It improves the convergence properties of the cyclostationary natural gradient algorithm (CSNGA) by employing a gradient adaptive learning rate whose value changes in response to some change in the filter parameters. Experimental results demonstrate the improved behaviour of the approach.
Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03). 2003 IEEE International Conference on; 05/2003 · 4.63 Impact Factor
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ABSTRACT: A wavelet domain approach to blind source separation which avoids
the permutation problem in transfer domain operation, and yields
improved convergence rate for certain non-stationary signals, is
introduced. The wavelet transform also facilitates noise reduction,
further improving performance of the natural gradient algorithm
Electronics Letters 08/2002; · 0.96 Impact Factor
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ABSTRACT: A new approach to blind source separation of cyclostationary
sources is introduced which incorporates a cyclic pre-whitening
operation within the learning rule, and thereby provides a new member of
the family of natural gradient algorithms. The technique improves the
convergence properties of the natural gradient algorithm for complex
valued, cyclostationary signals. Simulations show the improved
convergence speed of the approach
Electronics Letters 08/2002; · 0.96 Impact Factor
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ABSTRACT: A combined Kalman filter (KF) and natural gradient algorithm (NGA) approach is proposed to address the problem of blind source separation (BSS) in time-varying environments, in particular for binary distributed signals. In situations where the mixing channel is nonstationary, the performance of the NGA is often poor. Typically, in such cases, an adaptive learning rate is used to help the NGA track the changes in the environment. The Kalman filter, on the other hand, is the optimal, minimum mean square error method for tracking certain non-stationarity. Experimental results are presented, and suggest that the combined approach performs significantly better than NGA in the presence of both continuous and abrupt non-stationarities
Acoustics, Speech, and Signal Processing, 2001. Proceedings. (ICASSP '01). 2001 IEEE International Conference on; 02/2001
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ABSTRACT: A combined Kalman filter (KF) and natural gradient algorithm (NGA) approach is proposed to address the problem of blind source separation (BSS) in time-varying environments, in particular for binary distributed signals. In situations where the mixing channel is nonstationary, the performance of NGA is often poor. Typically, in such cases, an adaptive learning rate is used to help NGA trackthechanges in the environment. The Kalman filter, on the other hand, is the optimal minimum mean square error method for tracking certain non-stationarity. Experimental results are presented, and suggest that the combined approach performs significantly better than NGA in the presence of both continuous and abrupt non-stationarities.
11/2000;
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ABSTRACT: The use of adaptive noise cancellers (ANCs) to reduce the noise level prior to source separation is investigated in this paper. The foetal electrocardiogram (ECG) extraction prob-lem in particular is addressed, which as well as with noise, is compounded by the non-stationary nature of the measure-ments. Consequently, computer simulations show that the combined Kalman filter and natural gradient algorithm [1], cascaded with a parallel ANC network, leads to a technique that can significantly improve separation performance. More-over, it is shown that in some cases, the performance of the method is better than that of the JADE algorithm [2].
