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Radu Balan - One of the best experts on this subject based on the ideXlab platform.

  • generalized sparse signal mixing model and application to noisy blind source separation
    International Conference on Acoustics Speech and Signal Processing, 2004
    Co-Authors: Justinian Rosca, C Borss, Radu Balan
    Abstract:

    Sparse constraints on signal decompositions are justified by typical sensor data used in a variety of signal processing fields such as acoustics, medical imaging, or wireless, but moreover can lead to more effective algorithms. The specific sparseness assumption used in this work is that the maximum number of statistically independent sources active at any time and Frequency Point in a mixture of signals is small. This is shown to result from an assumption of sparseness of the sources themselves, and allows us to solve the maximum likelihood formulation of the noninstantaneous acoustic mixing source estimation problem. We consider an additive noise mixing model with an arbitrary number of sensors and possibly more sources than sensors, when sources satisfy the sparseness assumption above. The solution obtained is applicable to an arbitrary number of microphones and sources, but works best when the number of sources simultaneously active at any time Frequency Point is a small fraction of the total number of sources.

  • ICASSP (3) - Generalized sparse signal mixing model and application to noisy blind source separation
    2004 IEEE International Conference on Acoustics Speech and Signal Processing, 1
    Co-Authors: Justinian Rosca, C Borss, Radu Balan
    Abstract:

    Sparse constraints on signal decompositions are justified by typical sensor data used in a variety of signal processing fields such as acoustics, medical imaging, or wireless, but moreover can lead to more effective algorithms. The specific sparseness assumption used in this work is that the maximum number of statistically independent sources active at any time and Frequency Point in a mixture of signals is small. This is shown to result from an assumption of sparseness of the sources themselves, and allows us to solve the maximum likelihood formulation of the noninstantaneous acoustic mixing source estimation problem. We consider an additive noise mixing model with an arbitrary number of sensors and possibly more sources than sensors, when sources satisfy the sparseness assumption above. The solution obtained is applicable to an arbitrary number of microphones and sources, but works best when the number of sources simultaneously active at any time Frequency Point is a small fraction of the total number of sources.

Justinian Rosca - One of the best experts on this subject based on the ideXlab platform.

  • generalized sparse signal mixing model and application to noisy blind source separation
    International Conference on Acoustics Speech and Signal Processing, 2004
    Co-Authors: Justinian Rosca, C Borss, Radu Balan
    Abstract:

    Sparse constraints on signal decompositions are justified by typical sensor data used in a variety of signal processing fields such as acoustics, medical imaging, or wireless, but moreover can lead to more effective algorithms. The specific sparseness assumption used in this work is that the maximum number of statistically independent sources active at any time and Frequency Point in a mixture of signals is small. This is shown to result from an assumption of sparseness of the sources themselves, and allows us to solve the maximum likelihood formulation of the noninstantaneous acoustic mixing source estimation problem. We consider an additive noise mixing model with an arbitrary number of sensors and possibly more sources than sensors, when sources satisfy the sparseness assumption above. The solution obtained is applicable to an arbitrary number of microphones and sources, but works best when the number of sources simultaneously active at any time Frequency Point is a small fraction of the total number of sources.

  • ICASSP (3) - Generalized sparse signal mixing model and application to noisy blind source separation
    2004 IEEE International Conference on Acoustics Speech and Signal Processing, 1
    Co-Authors: Justinian Rosca, C Borss, Radu Balan
    Abstract:

    Sparse constraints on signal decompositions are justified by typical sensor data used in a variety of signal processing fields such as acoustics, medical imaging, or wireless, but moreover can lead to more effective algorithms. The specific sparseness assumption used in this work is that the maximum number of statistically independent sources active at any time and Frequency Point in a mixture of signals is small. This is shown to result from an assumption of sparseness of the sources themselves, and allows us to solve the maximum likelihood formulation of the noninstantaneous acoustic mixing source estimation problem. We consider an additive noise mixing model with an arbitrary number of sensors and possibly more sources than sensors, when sources satisfy the sparseness assumption above. The solution obtained is applicable to an arbitrary number of microphones and sources, but works best when the number of sources simultaneously active at any time Frequency Point is a small fraction of the total number of sources.

Patrick Pérez - One of the best experts on this subject based on the ideXlab platform.

  • Informed source separation via compressive graph signal sampling
    2017
    Co-Authors: Gilles Puy, Alexey Ozerov, Ngoc Duong, Patrick Pérez
    Abstract:

    We propose a novel informed source separation method for audio object coding based on a recent sampling theory for smooth signals on graphs. Assuming that only one source is active at each time-Frequency Point, we compute an ideal map indicating which source is active at each time-Frequency Point at the encoder. This map is then sampled with a compressive graph signal sampling strategy that guarantees accurate and stable recovery at the decoder. The graph is built using feature vectors, computed using non-negative matrix factorization, that allows us to connect similar source activations in the time-Frequency plane. We show that the proposed approach performs better than state-of-the-art methods at low bitrate.

  • ICASSP - Informed source separation via compressive graph signal sampling
    2017 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2017
    Co-Authors: Gilles Puy, Alexey Ozerov, Ngoc Q. K. Duong, Patrick Pérez
    Abstract:

    We propose a novel informed source separation method for audio object coding based on a recent sampling theory for smooth signals on graphs. Assuming that only one source is active at each time-Frequency Point, we compute an ideal map indicating which source is active at each time-Frequency Point at the encoder. This map is then sampled with a compressive graph signal sampling strategy that guarantees accurate and stable recovery at the decoder. The graph is built using feature vectors, computed using non-negative matrix factorization, that allows us to connect similar source activations in the time-Frequency plane. We show that the proposed approach performs better than state-of-the-art methods at low bitrate.

C Borss - One of the best experts on this subject based on the ideXlab platform.

  • generalized sparse signal mixing model and application to noisy blind source separation
    International Conference on Acoustics Speech and Signal Processing, 2004
    Co-Authors: Justinian Rosca, C Borss, Radu Balan
    Abstract:

    Sparse constraints on signal decompositions are justified by typical sensor data used in a variety of signal processing fields such as acoustics, medical imaging, or wireless, but moreover can lead to more effective algorithms. The specific sparseness assumption used in this work is that the maximum number of statistically independent sources active at any time and Frequency Point in a mixture of signals is small. This is shown to result from an assumption of sparseness of the sources themselves, and allows us to solve the maximum likelihood formulation of the noninstantaneous acoustic mixing source estimation problem. We consider an additive noise mixing model with an arbitrary number of sensors and possibly more sources than sensors, when sources satisfy the sparseness assumption above. The solution obtained is applicable to an arbitrary number of microphones and sources, but works best when the number of sources simultaneously active at any time Frequency Point is a small fraction of the total number of sources.

  • ICASSP (3) - Generalized sparse signal mixing model and application to noisy blind source separation
    2004 IEEE International Conference on Acoustics Speech and Signal Processing, 1
    Co-Authors: Justinian Rosca, C Borss, Radu Balan
    Abstract:

    Sparse constraints on signal decompositions are justified by typical sensor data used in a variety of signal processing fields such as acoustics, medical imaging, or wireless, but moreover can lead to more effective algorithms. The specific sparseness assumption used in this work is that the maximum number of statistically independent sources active at any time and Frequency Point in a mixture of signals is small. This is shown to result from an assumption of sparseness of the sources themselves, and allows us to solve the maximum likelihood formulation of the noninstantaneous acoustic mixing source estimation problem. We consider an additive noise mixing model with an arbitrary number of sensors and possibly more sources than sensors, when sources satisfy the sparseness assumption above. The solution obtained is applicable to an arbitrary number of microphones and sources, but works best when the number of sources simultaneously active at any time Frequency Point is a small fraction of the total number of sources.

Gilles Puy - One of the best experts on this subject based on the ideXlab platform.

  • Informed source separation via compressive graph signal sampling
    2017
    Co-Authors: Gilles Puy, Alexey Ozerov, Ngoc Duong, Patrick Pérez
    Abstract:

    We propose a novel informed source separation method for audio object coding based on a recent sampling theory for smooth signals on graphs. Assuming that only one source is active at each time-Frequency Point, we compute an ideal map indicating which source is active at each time-Frequency Point at the encoder. This map is then sampled with a compressive graph signal sampling strategy that guarantees accurate and stable recovery at the decoder. The graph is built using feature vectors, computed using non-negative matrix factorization, that allows us to connect similar source activations in the time-Frequency plane. We show that the proposed approach performs better than state-of-the-art methods at low bitrate.

  • ICASSP - Informed source separation via compressive graph signal sampling
    2017 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2017
    Co-Authors: Gilles Puy, Alexey Ozerov, Ngoc Q. K. Duong, Patrick Pérez
    Abstract:

    We propose a novel informed source separation method for audio object coding based on a recent sampling theory for smooth signals on graphs. Assuming that only one source is active at each time-Frequency Point, we compute an ideal map indicating which source is active at each time-Frequency Point at the encoder. This map is then sampled with a compressive graph signal sampling strategy that guarantees accurate and stable recovery at the decoder. The graph is built using feature vectors, computed using non-negative matrix factorization, that allows us to connect similar source activations in the time-Frequency plane. We show that the proposed approach performs better than state-of-the-art methods at low bitrate.