Maria G. Jafari

Gheorghe Asachi Technical University of Iasi, Socola, Iaşi, Romania

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Publications (54)40.86 Total impact

  • [Show abstract] [Hide abstract]
    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. DOI:10.1109/EMBC.2012.6346705
  • Maria G. Jafari · Mark D. Plumbley
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    ABSTRACT: The papers at this Convention have been selected on the basis of a submitted abstract and extended precis that have been peer reviewed by at least two qualified anonymous reviewers. This convention paper has been reproduced from the author’s advance manuscript, without editing, corrections, or consideration by the Review Board. The AES takes no responsibility for the contents. Additional papers may be obtained by sending request and remittance to Audio
  • Maria G. Jafari · Mark D. Plumbley
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    ABSTRACT: role of high frequencies in convolutive blind
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    Emmanuel Vincent · Maria G. Jafari · Mark D. Plumbley
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    ABSTRACT: Evaluating audio source separation algorithms means rat- ing the quality or intelligibility of separated source sig- nals. While objective criteria fail to account for all audi- tory phenomena so far, precise subjective ratings can be obtained by means of listening tests. In practice, the accu- racy and the reproducibility of these tests depend on sev- eral design issues. In this paper, we discuss some of these issues based on ongoing research in other areas of audio signal processing. We propose preliminary guidelines to evaluate the basic audio quality of separated sources and provide an example of their application using a free Mat- lab graphical interface.
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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; 20(3-20):922 - 932. DOI:10.1109/TASL.2011.2168211 · 2.63 Impact Factor
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    ABSTRACT: adaptive stereo basis method for
  • Maria G. Jafari · Mark D. Plumbley · Mike E. Davies
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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. Index Terms — Orthogonal transform, sparse dictionary, adaptive dictionary, source separation.
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    ABSTRACT: We consider the problem of convolutive blind source sep-aration of audio mixtures. We propose an Adaptive Stereo Basis (ASB) method based on learning a set of basis vec-tors pairs from the time-domain stereo mixtures. The basis vector pairs are clustered using estimated directions of ar-rival (DOAs) such that each basis vector pair is associated with one source. The ASB method is compared with the DUET algorithm on convolutive speech mixtures at dif-ferent reverberation times and noise levels.
  • Maria G. Jafari · Mark D. Plumbley
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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. Index Terms — Approximation methods, Signal analysis, Signal representations, Discrete transforms
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    Nicolae Cleju · M.G. Jafari · M.D. Plumbley
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    ABSTRACT: Analysis based reconstruction has recently been introduced as an alternative to the well-known synthesis sparsity model used in a variety of signal processing areas. In this paper we convert the analysis exact-sparse reconstruction problem to an equivalent synthesis recovery problem with a set of additional constraints. We are therefore able to use existing synthesis-based algorithms for analysis-based exact-sparse recovery. We call this the Analysis-By-Synthesis (ABS) approach. We evaluate our proposed approach by comparing it against the recent Greedy Analysis Pursuit (GAP) analysis-based recovery algorithm. The results show that our approach is a viable option for analysis-based reconstruction, while at the same time allowing many algorithms that have been developed for synthesis reconstruction to be directly applied for analysis reconstruction as well.
    Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on; 03/2012
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    N. Cleju · M.G. Jafari · M.D. Plumbley
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    ABSTRACT: The analysis sparsity model is a recently introduced alternative to the standard synthesis sparsity model frequently used in signal processing. However, the exact conditions when analysis-based recovery is better than synthesis recovery are still not known. This paper constitutes an initial investigation into determining when one model is better than the other, under similar conditions. We perform separate analysis and synthesis recovery on a large number of randomly generated signals that are simultaneously sparse in both models and we compare the average reconstruction errors with both recovery methods. The results show that analysis-based recovery is the better option for a large number of signals, but it is less robust with signals that are only approximately sparse or when fewer measurements are available.
    Signal Processing Conference (EUSIPCO), 2012 Proceedings of the 20th European; 01/2012
  • Maria Jafari · Mark D. Plumbley
  • C. Barabasa · M. Jafari · M.D. Plumbley
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    ABSTRACT: We present a robust method for the detection of the first and second heart sounds (s1 and s2), without ECG reference, based on a music beat tracking algorithm. An intermediate representation of the input signal is first calculated by using an onset detection function based on complex spectral difference. A music beat tracking algorithm is then used to determine the location of the first heart sound. The beat tracker works in two steps, it first calculates the beat period and then finds the temporal beat alignment. Once the first sound is detected, inverse Gaussian weights are applied to the onset function on the detected positions and the algorithm is run again to find the second heart sound. At the last step s1 and s2 labels are attributed to the detected sounds. The algorithm was evaluated in terms of location accuracy as well as sensitivity and specificity and the results showed good results even in the presence of murmurs or noisy signals.
    Electronics and Telecommunications (ISETC), 2012 10th International Symposium on; 01/2012
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    M.G. Jafari · M.D. Plumbley
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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; 5(5-5):1025 - 1031. DOI:10.1109/JSTSP.2011.2157892 · 3.63 Impact Factor
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    ABSTRACT: We consider the separation of sources when only one movable sensor is available to record a set of mixtures at distinct locations. A single mixture signal is acquired, which is firstly segmented. Then, based on the assumption that the underlying sources are temporally periodic, we align the resulting signals and form a measurement vector on which source separation can be performed. We demonstrate that this approach can successfully recover the original sources both when working with simulated data, and for a real problem of heart sound separation.
    2011 7th International Symposium on Image and Signal Processing and Analysis (ISPA); 09/2011
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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.
    Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011, May 22-27, 2011, Prague Congress Center, Prague, Czech Republic; 05/2011
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    ABSTRACT: We present a novel sparse representation based approach for the restoration of clipped audio signals. In the proposed approach, the clipped signal is decomposed into overlapping frames and the declipping problem is formulated as an inverse problem, per audio frame. This problem is further solved by a constrained matching pursuit algorithm, that exploits the sign pattern of the clipped samples and their maximal absolute value. Performance evaluation with a collection of music and speech signals demonstrate superior results compared to existing algorithms, over a wide range of clipping levels.
    Acoustics, Speech, and Signal Processing, 1988. ICASSP-88., 1988 International Conference on 01/2011; DOI:10.1109/ICASSP.2011.5946407 · 4.63 Impact Factor
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    Andrew Nesbit · Maria Jafari · Mark D. Plumbley
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    ABSTRACT: Most sound scenes result from the superposition of several sources, which can be separately perceived and analyzed by human listeners. Source separation aims to provide machine listeners with similar skills by extracting the sounds of individual sources from a given scene. Existing separation systems operate either by emulating the human auditory system or by inferring the parameters of probabilistic sound models. In this chapter, we focus on the latter approach and provide a joint overview of established and recent models, including independent component analysis, local time-frequency models and spectral template-based models. We show that most models are instances of one of the following two general paradigms: linear modeling or variance modeling. We compare the merits of either paradigm and report objective performance figures. We conclude by discussing promising combinations of probabilistic priors and inference algorithms that could form the basis of future state-of-the-art systems.

Publication Stats

304 Citations
40.86 Total Impact Points

Institutions

  • 2012
    • Gheorghe Asachi Technical University of Iasi
      Socola, Iaşi, Romania
    • Technion - Israel Institute of Technology
      H̱efa, Haifa District, Israel
  • 2005–2011
    • Queen Mary, University of London
      • School of Electronic Engineering and Computer Science
      Londinium, England, United Kingdom
  • 2002–2004
    • King's College London
      • Centre for Digital Signal Processing Research
      Londinium, England, United Kingdom
  • 2001
    • Imperial College London
      • Department of Electrical and Electronic Engineering
      London, ENG, United Kingdom