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Limited By:	Author = O'Grady, Paul D.;

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Convolutive non-negative matrix factorisation with a sparseness constraint (2006)

Pearlmutter, Barak A.; O'Grady, Paul D.

Convolutive non-negative matrix factorisation with a sparseness constraint (2006)

Pearlmutter, Barak A.; O'Grady, Paul D.

Abstract:

Discovering a representation which allows auditory data to be parsimoniously represented is useful for many machine learning and signal processing tasks. Such a representation can be constructed by non-negative matrix factorisation (NMF), a method for finding parts-based representations of non-negative data. We present an extension to NMF that is convolutive and includes a sparseness constraint. In combination with a spectral magnitude transform, this method discovers auditory objects and their associated sparse activation patterns.

http://mural.maynoothuniversity.ie/1375/

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Discovering Convolutive Speech Phones Using Sparseness and Non-negativity (2007)

O'Grady, Paul D.; Pearlmutter, Barak A.

Discovering Convolutive Speech Phones Using Sparseness and Non-negativity (2007)

O'Grady, Paul D.; Pearlmutter, Barak A.

Abstract:

Discovering a representation that allows auditory data to be parsimoniously represented is useful for many machine learning and signal processing tasks. Such a representation can be constructed by Non-negative Matrix Factorisation (NMF), which is a method for finding parts-based representations of non-negative data. Here, we present a convolutive NMF algorithm that includes a sparseness constraint on the activations and has multiplicative updates. In combination with a spectral magnitude transform of speech, this method extracts speech phones that exhibit sparse activation patterns, which we use in a supervised separation scheme for monophonic mixtures.

http://mural.maynoothuniversity.ie/10244/

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Discovering Convolutive Speech Phones using Sparseness and Non-Negativity Constraints (2007)

O'Grady, Paul D.; Pearlmutter, Barak A.

Discovering Convolutive Speech Phones using Sparseness and Non-Negativity Constraints (2007)

O'Grady, Paul D.; Pearlmutter, Barak A.

Abstract:

Discovering a representation that allows auditory data to be parsimoniously represented is useful for many machine learning and signal processing tasks. Such a representation can be constructed by Non-negative Matrix Factorisation (NMF), which is a method for finding parts-based representations of non-negative data. Here, we present an extension to convolutive NMF that includes a sparseness constraint. In combination with a spectral magnitude transform of speech, this method extracts speech phones (and their associated sparse activation patterns), which we use in a supervised separation scheme for monophonic mixtures.

http://mural.maynoothuniversity.ie/1313/

Marked Mark

Discovering speech phones using convolutive non-negative matrix factorisation with a sparseness constraint (2008)

O'Grady, Paul D.; Pearlmutter, Barak A.

Discovering speech phones using convolutive non-negative matrix factorisation with a sparseness constraint (2008)

O'Grady, Paul D.; Pearlmutter, Barak A.

Abstract:

Discovering a representation that allows auditory data to be parsimoniously represented is useful for many machine learning and signal processing tasks. Such a representation can be constructed by Non-negative Matrix Factorisation (NMF), a method for finding parts-based representations of non-negative data. Here, we present an extension to convolutive NMF that includes a sparseness constraint, where the resultant algorithm has multiplicative updates and utilises the beta divergence as its reconstruction objective. In combination with a spectral magnitude transform of speech, this method discovers auditory objects that resemble speech phones along with their associated sparse activation patterns. We use these in a supervised separation scheme for monophonic mixtures, finding improved separation performance in comparison to classic convolutive NMF.

http://mural.maynoothuniversity.ie/1697/

Marked Mark

Discovering speech phones using convolutive non-negative matrix factorisation with a sparseness constraint (2008)

O'Grady, Paul D.; Pearlmutter, Barak A.

Discovering speech phones using convolutive non-negative matrix factorisation with a sparseness constraint (2008)

O'Grady, Paul D.; Pearlmutter, Barak A.

Abstract:

Discovering a representation that allows auditory data to be parsimoniously represented is useful for many machine learning and signal processing tasks. Such a representation can be constructed by Non-negative Matrix Factorisation (NMF), a method for finding parts-based representations of non-negative data. Here, we present an extension to convolutive NMF that includes a sparseness constraint, where the resultant algorithm has multiplicative updates and utilises the beta divergence as its reconstruction objective. In combination with a spectral magnitude transform of speech, this method discovers auditory objects that resemble speech phones along with their associated sparse activation patterns. We use these in a supervised separation scheme for monophonic mixtures, finding improved separation performance in comparison to classic convolutive NMF.

http://mural.maynoothuniversity.ie/1685/

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Hard-LOST: Modified k-Means for Oriented Lines (2004)

O'Grady, Paul D.; Pearlmutter, Barak A.

Hard-LOST: Modified k-Means for Oriented Lines (2004)

O'Grady, Paul D.; Pearlmutter, Barak A.

Abstract:

Robust clustering of data into linear subspaces is a common problem. Here we treat clustering into one-dimensional subspaces that cross the origin. This problem arises in blind source separation, where the subspaces correspond directly to columns of a mixing matrix. We present an algorithm that identifies these subspaces using a modified k-means procedure, where line orientations and distances from a line replace the cluster centres and distance from cluster centres of conventional k-means. This method, combined with a transformation into a sparse domain and an L1-norm optimisation, constitutes a blind source separation algorithm for the under-determined case.

http://mural.maynoothuniversity.ie/2052/

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Survey of Sparse and Non-Sparse Methods in Source Separation (2005)

O'Grady, Paul D.; Pearlmutter, Barak A.; Rickard, Scott T.

Survey of Sparse and Non-Sparse Methods in Source Separation (2005)

O'Grady, Paul D.; Pearlmutter, Barak A.; Rickard, Scott T.

Abstract:

Source separation arises in a variety of signal processing applications, ranging from speech processing to medical image analysis. The separation of a superposition of multiple signals is accomplished by taking into account the structure of the mixing process and by making assumptions about the sources. When the information about the mixing process and sources is limited, the problem is called ‘‘blind’. By assuming that the sources can be represented sparsely in a given basis, recent research has demonstrated that solutions to previously problematic blind source separation problems can be obtained. In some cases, solutions are possible to problems intractable by previous non-sparse methods. Indeed, sparse methods provide a powerful approach to the separation of linear mixtures of independent data. This paper surveys the recent arrival of sparse blind source separation methods and the previously existing nonsparse methods, providing insights and appropriate hooks into theliterature a...

http://mural.maynoothuniversity.ie/5502/

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The LOST Algorithm: finding lines and separating speech mixtures (2008)

O'Grady, Paul D.; Pearlmutter, Barak A.

The LOST Algorithm: finding lines and separating speech mixtures (2008)

O'Grady, Paul D.; Pearlmutter, Barak A.

Abstract:

Robust clustering of data into linear subspaces is a frequently encountered problem. Here, we treat clustering of one-dimensional subspaces that cross the origin. This problem arises in blind source separation, where the subspaces correspond directly to columns of a mixing matrix. We propose the LOST algorithm, which identifies such subspaces using a procedure similar in spirit to EM. This line finding procedure combined with a transformation into a sparse domain and an L1-norm minimisation constitutes a blind source separation algorithm for the separation of instantaneous mixtures with an arbitrary number of mixtures and sources. We perform an extensive investigation on the general separation performance of the LOST algorithm using randomly generated mixtures, and empirically estimate the performance of the algorithm in the presence of noise. Furthermore, we implement a simple scheme whereby the number of sources present in the mixtures can be detected automatically

http://mural.maynoothuniversity.ie/1699/