144 research outputs found

    Convolutive Blind Source Separation Methods

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    In this chapter, we provide an overview of existing algorithms for blind source separation of convolutive audio mixtures. We provide a taxonomy, wherein many of the existing algorithms can be organized, and we present published results from those algorithms that have been applied to real-world audio separation tasks

    On The Positive Definiteness of Polarity Coincidence Correlation Coefficient Matrix

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    Polarity coincidence correlator (PCC), when used to estimate the covariance matrix on an element-by-element basis, may not yield a positive semi-definite (PSD) estimate. Devlin et al. [1], claimed that element-wise PCC is not guaranteed to be PSD in dimensions p>3 for real signals. However, no justification or proof was available on this issue. In this letter, it is proved that for real signals with p<=3 and for complex signals with p<=2, a PSD estimate is guaranteed. Counterexamples are presented for higher dimensions which yield invalid covariance estimates.Comment: IEEE Signal Processing Letters, Volume 15, pp. 73-76, 200

    Independent Component Analysis in a convoluted world

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    Source Separation for Hearing Aid Applications

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    Ecosystem Monitoring and Port Surveillance Systems

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    International audienceIn this project, we should build up a novel system able to perform a sustainable and long term monitoring coastal marine ecosystems and enhance port surveillance capability. The outcomes will be based on the analysis, classification and the fusion of a variety of heterogeneous data collected using different sensors (hydrophones, sonars, various camera types, etc). This manuscript introduces the identified approaches and the system structure. In addition, it focuses on developed techniques and concepts to deal with several problems related to our project. The new system will address the shortcomings of traditional approaches based on measuring environmental parameters which are expensive and fail to provide adequate large-scale monitoring. More efficient monitoring will also enable improved analysis of climate change, and provide knowledge informing the civil authority's economic relationship with its coastal marine ecosystems

    A new second order method for blind signal separation from convolutive mixtures

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    This paper presents a new approach to separate colored signals mixed by FIR (finite impulse response) and MIMO (multiple-input multiple-output) channels. A cost function is proposed by employing linear constrainit to the de mixing vectors. The linear constraint is shown to be sufficient for avoiding trivial solution. The minimization of the cost function is performed using the Lagrangian method. Simulation results demonstrate the performance of the algorithm.<br /

    Non-orthogonal joint block diagonalization based on the LU or QR factorizations for convolutive blind source separation

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    This article addresses the problem of blind source separation, in which the source signals are most often of the convolutive mixtures, and moreover, the source signals cannot satisfy independent identical distribution generally. One kind of prevailing and representative approaches for overcoming these difficulties is joint block diagonalization (JBD) method. To improve present JBD methods, we present a class of simple Jacobi-type JBD algorithms based on the LU or QR factorizations. Using Jacobi-type matrices we can replace high dimensional minimization problems with a sequence of simple one-dimensional problems. The novel methods are more general i.e. the orthogonal, positive definite or symmetric matrices and a preliminary whitening stage is no more compulsorily required, and further, the convergence is also guaranteed. The performance of the proposed algorithms, compared with the existing state-of-the-art JBD algorithms, is evaluated with computer simulations and vibration experimental. The results of numerical examples demonstrate that the robustness and effectiveness of the two novel algorithms provide a significant improvement i.e., yield less convergence time, higher precision of convergence, better success rate of block diagonalization. And the proposed algorithms are effective in separating the vibration signals of convolutive mixtures
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