The Experts below are selected from a list of 61917 Experts worldwide ranked by ideXlab platform
Kazunobu Kondo - One of the best experts on this subject based on the ideXlab platform.
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music Signal Separation using supervised nmf with all pole model based discriminative basis deformation
European Signal Processing Conference, 2016Co-Authors: Hiroaki Nakajima, Nobutaka Ono, Daichi Kitamura, Hiroshi Saruwatari, Yu Takahashi, Norihiro Takamune, Shoichi Koyama, Kazunobu KondoAbstract:In this paper, we address the music Signal Separation problem and propose a new supervised nonnegative matrix factorization (SNMF) algorithm employing the deformation of a spectral supervision basis trained in advance. Conventional SNMF has a problem that the Separation accuracy is degraded by a mismatch between the trained basis and the spectrogram of the actual target sound in open data. To reduce the mismatch problem, we propose a new method with two features. First, we introduce a deformation with an all-pole model that is optimized to make the trained basis fit the spectrogram of the target Signal, even if the true target component is hidden in the observed mixture. Next, to avoid an excess deformation, we limit the degree of freedom in the deformation by performing discriminative training. Our experimental evaluation reveals that the proposed method outperforms conventional SNMFs.
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multichannel Signal Separation combining directional clustering and nonnegative matrix factorization with spectrogram restoration
IEEE Transactions on Audio Speech and Language Processing, 2015Co-Authors: Daichi Kitamura, Hiroshi Saruwatari, Kazunobu Kondo, Yu Takahashi, Hirokazu Kameoka, Satoshi NakamuraAbstract:In this paper, to address problems in multichannel music Signal Separation, we propose a new hybrid method that combines directional clustering and advanced nonnegative matrix factorization (NMF). The aims of multichannel music Signal Separation technology is to extract a specific target Signal from observed multichannel Signals that contain multiple instrumental sounds. In previous studies, various methods using NMF have been proposed, but many problems remain including poor Separation accuracy and lack of robustness. To solve these problems, we propose a new supervised NMF (SNMF) with spectrogram restoration and a hybrid method that concatenates the proposed SNMF after directional clustering. Via the extrapolation of supervised spectral bases, the proposed SNMF attempts both target Signal Separation and reconstruction of the lost target components, which are generated by preceding directional clustering. In addition, we experimentally reveal the trade-off between Separation and extrapolation abilities and propose a new scheme for adaptive divergence, where the optimal divergence can be automatically changed in each time frame according to the local spatial conditions. The results of an evaluation experiment show that our proposed hybrid method outperforms the conventional music Signal Separation methods.
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hybrid multichannel Signal Separation using supervised nonnegative matrix factorization with spectrogram restoration
Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, 2014Co-Authors: Daichi Kitamura, Hiroshi Saruwatari, Kazunobu Kondo, Yu Takahashi, Satoshi Nakamura, Hirokazu KameokaAbstract:In this paper, we propose a new hybrid method that concatenates directional clustering and advanced nonnegative matrix factorization (NMF) for the purpose of the specific sound extraction from the multichannel music Signal. Multichannel music Signal Separation technology is aimed to extract a specific target Signal from observed multichannel Signals that contain multiple instrumental sounds. In the previous studies, various methods using NMF have been proposed, but they remain many problems, e.g., poor convergence in update rules in NMF and lack of robustness. To solve these problems, we propose a new supervised NMF (SNMF) with spectrogram restoration and its hybrid method that concatenates the proposed SNMF after directional clustering. Via extrapolation of supervised spectral bases, the proposed SNMF attempts both target Signal Separation and reconstruction of the lost target components, which are generated by preceding directional clustering. In addition, we theoretically reveal the trade-off between Separation and extrapolation abilities and propose a new scheme for multi-divergence, where optimal divergence can be automatically changed in each time frame according to the local spatial conditions. The results of an evaluation experiment show that our proposed hybrid method outperforms the conventional music Signal Separation methods.
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divergence optimization in nonnegative matrix factorization with spectrogram restoration for multichannel Signal Separation
Hands-free Speech Communication and Microphone Arrays (HSCMA) 2014 4th Joint Workshop on, 2014Co-Authors: Daichi Kitamura, Hiroshi Saruwatari, Kazunobu Kondo, Yu Takahashi, Satoshi Nakamura, Hirokazu KameokaAbstract:In this paper, we address an optimization issue for the divergence in supervised nonnegative matrix factorization with spectrogram restoration, which has been proposed for addressing multichannel Signal Separation. This method separates non-target components and reconstructs some missing data caused by preceding spatial clustering via supervised basis extrapolation. In our previous study, we only used a limited type of divergence, whereas the divergence selection is essential. Therefore, we extend this method to a more generalized form and give a theoretical analysis of the divergence optimization, where we reveal the trade-off between Separation and extrapolation abilities.
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music Signal Separation based on bayesian spectral amplitude estimator with automatic target prior adaptation
International Conference on Acoustics Speech and Signal Processing, 2014Co-Authors: Yuki Murota, Daichi Kitamura, Hiroshi Saruwatari, Yu Takahashi, Shunsuke Nakai, Satoshi Nakamura, Kazunobu KondoAbstract:In this paper, we propose a new approach for addressing music Signal Separation based on the generalized Bayesian estimator with automatic prior adaptation. This method consists of three parts, namely, the generalized MMSE-STSA estimator with a flexible target Signal prior, the NMF-based dynamic interference spectrogram estimator, and closed-form parameter estimation for the statistical model of the target Signal based on higher-order statistics. The statistical model parameter of the hidden target Signal can be detected automatically for optimal Bayesian estimation with online target-Signal prior adaptation. Our experimental evaluation can show the efficacy of the proposed method.
Hiroshi Saruwatari - One of the best experts on this subject based on the ideXlab platform.
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music Signal Separation using supervised nmf with all pole model based discriminative basis deformation
European Signal Processing Conference, 2016Co-Authors: Hiroaki Nakajima, Nobutaka Ono, Daichi Kitamura, Hiroshi Saruwatari, Yu Takahashi, Norihiro Takamune, Shoichi Koyama, Kazunobu KondoAbstract:In this paper, we address the music Signal Separation problem and propose a new supervised nonnegative matrix factorization (SNMF) algorithm employing the deformation of a spectral supervision basis trained in advance. Conventional SNMF has a problem that the Separation accuracy is degraded by a mismatch between the trained basis and the spectrogram of the actual target sound in open data. To reduce the mismatch problem, we propose a new method with two features. First, we introduce a deformation with an all-pole model that is optimized to make the trained basis fit the spectrogram of the target Signal, even if the true target component is hidden in the observed mixture. Next, to avoid an excess deformation, we limit the degree of freedom in the deformation by performing discriminative training. Our experimental evaluation reveals that the proposed method outperforms conventional SNMFs.
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multichannel Signal Separation combining directional clustering and nonnegative matrix factorization with spectrogram restoration
IEEE Transactions on Audio Speech and Language Processing, 2015Co-Authors: Daichi Kitamura, Hiroshi Saruwatari, Kazunobu Kondo, Yu Takahashi, Hirokazu Kameoka, Satoshi NakamuraAbstract:In this paper, to address problems in multichannel music Signal Separation, we propose a new hybrid method that combines directional clustering and advanced nonnegative matrix factorization (NMF). The aims of multichannel music Signal Separation technology is to extract a specific target Signal from observed multichannel Signals that contain multiple instrumental sounds. In previous studies, various methods using NMF have been proposed, but many problems remain including poor Separation accuracy and lack of robustness. To solve these problems, we propose a new supervised NMF (SNMF) with spectrogram restoration and a hybrid method that concatenates the proposed SNMF after directional clustering. Via the extrapolation of supervised spectral bases, the proposed SNMF attempts both target Signal Separation and reconstruction of the lost target components, which are generated by preceding directional clustering. In addition, we experimentally reveal the trade-off between Separation and extrapolation abilities and propose a new scheme for adaptive divergence, where the optimal divergence can be automatically changed in each time frame according to the local spatial conditions. The results of an evaluation experiment show that our proposed hybrid method outperforms the conventional music Signal Separation methods.
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hybrid multichannel Signal Separation using supervised nonnegative matrix factorization with spectrogram restoration
Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, 2014Co-Authors: Daichi Kitamura, Hiroshi Saruwatari, Kazunobu Kondo, Yu Takahashi, Satoshi Nakamura, Hirokazu KameokaAbstract:In this paper, we propose a new hybrid method that concatenates directional clustering and advanced nonnegative matrix factorization (NMF) for the purpose of the specific sound extraction from the multichannel music Signal. Multichannel music Signal Separation technology is aimed to extract a specific target Signal from observed multichannel Signals that contain multiple instrumental sounds. In the previous studies, various methods using NMF have been proposed, but they remain many problems, e.g., poor convergence in update rules in NMF and lack of robustness. To solve these problems, we propose a new supervised NMF (SNMF) with spectrogram restoration and its hybrid method that concatenates the proposed SNMF after directional clustering. Via extrapolation of supervised spectral bases, the proposed SNMF attempts both target Signal Separation and reconstruction of the lost target components, which are generated by preceding directional clustering. In addition, we theoretically reveal the trade-off between Separation and extrapolation abilities and propose a new scheme for multi-divergence, where optimal divergence can be automatically changed in each time frame according to the local spatial conditions. The results of an evaluation experiment show that our proposed hybrid method outperforms the conventional music Signal Separation methods.
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divergence optimization in nonnegative matrix factorization with spectrogram restoration for multichannel Signal Separation
Hands-free Speech Communication and Microphone Arrays (HSCMA) 2014 4th Joint Workshop on, 2014Co-Authors: Daichi Kitamura, Hiroshi Saruwatari, Kazunobu Kondo, Yu Takahashi, Satoshi Nakamura, Hirokazu KameokaAbstract:In this paper, we address an optimization issue for the divergence in supervised nonnegative matrix factorization with spectrogram restoration, which has been proposed for addressing multichannel Signal Separation. This method separates non-target components and reconstructs some missing data caused by preceding spatial clustering via supervised basis extrapolation. In our previous study, we only used a limited type of divergence, whereas the divergence selection is essential. Therefore, we extend this method to a more generalized form and give a theoretical analysis of the divergence optimization, where we reveal the trade-off between Separation and extrapolation abilities.
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music Signal Separation based on bayesian spectral amplitude estimator with automatic target prior adaptation
International Conference on Acoustics Speech and Signal Processing, 2014Co-Authors: Yuki Murota, Daichi Kitamura, Hiroshi Saruwatari, Yu Takahashi, Shunsuke Nakai, Satoshi Nakamura, Kazunobu KondoAbstract:In this paper, we propose a new approach for addressing music Signal Separation based on the generalized Bayesian estimator with automatic prior adaptation. This method consists of three parts, namely, the generalized MMSE-STSA estimator with a flexible target Signal prior, the NMF-based dynamic interference spectrogram estimator, and closed-form parameter estimation for the statistical model of the target Signal based on higher-order statistics. The statistical model parameter of the hidden target Signal can be detected automatically for optimal Bayesian estimation with online target-Signal prior adaptation. Our experimental evaluation can show the efficacy of the proposed method.
Daichi Kitamura - One of the best experts on this subject based on the ideXlab platform.
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The 2016 Signal Separation Evaluation Campaign
Latent Variable Analysis and Signal Separation, 2017Co-Authors: Antoine Liutkus, Fabian-robert Stöter, Nobutaka Ono, Zafar Rafii, Daichi Kitamura, Bertrand Rivet, Nobutaka Ito, Julie FontecaveAbstract:In this paper, we report the results of the 2016 community-based Signal Separation Evaluation Campaign (SiSEC 2016). This edition comprises four tasks. Three focus on the Separation of speech and music audio recordings, while one concerns biomedical Signals. We summarize these tasks and the performance of the submitted systems, as well as provide a small discussion concerning future trends of SiSEC.
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music Signal Separation using supervised nmf with all pole model based discriminative basis deformation
European Signal Processing Conference, 2016Co-Authors: Hiroaki Nakajima, Nobutaka Ono, Daichi Kitamura, Hiroshi Saruwatari, Yu Takahashi, Norihiro Takamune, Shoichi Koyama, Kazunobu KondoAbstract:In this paper, we address the music Signal Separation problem and propose a new supervised nonnegative matrix factorization (SNMF) algorithm employing the deformation of a spectral supervision basis trained in advance. Conventional SNMF has a problem that the Separation accuracy is degraded by a mismatch between the trained basis and the spectrogram of the actual target sound in open data. To reduce the mismatch problem, we propose a new method with two features. First, we introduce a deformation with an all-pole model that is optimized to make the trained basis fit the spectrogram of the target Signal, even if the true target component is hidden in the observed mixture. Next, to avoid an excess deformation, we limit the degree of freedom in the deformation by performing discriminative training. Our experimental evaluation reveals that the proposed method outperforms conventional SNMFs.
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multichannel Signal Separation combining directional clustering and nonnegative matrix factorization with spectrogram restoration
IEEE Transactions on Audio Speech and Language Processing, 2015Co-Authors: Daichi Kitamura, Hiroshi Saruwatari, Kazunobu Kondo, Yu Takahashi, Hirokazu Kameoka, Satoshi NakamuraAbstract:In this paper, to address problems in multichannel music Signal Separation, we propose a new hybrid method that combines directional clustering and advanced nonnegative matrix factorization (NMF). The aims of multichannel music Signal Separation technology is to extract a specific target Signal from observed multichannel Signals that contain multiple instrumental sounds. In previous studies, various methods using NMF have been proposed, but many problems remain including poor Separation accuracy and lack of robustness. To solve these problems, we propose a new supervised NMF (SNMF) with spectrogram restoration and a hybrid method that concatenates the proposed SNMF after directional clustering. Via the extrapolation of supervised spectral bases, the proposed SNMF attempts both target Signal Separation and reconstruction of the lost target components, which are generated by preceding directional clustering. In addition, we experimentally reveal the trade-off between Separation and extrapolation abilities and propose a new scheme for adaptive divergence, where the optimal divergence can be automatically changed in each time frame according to the local spatial conditions. The results of an evaluation experiment show that our proposed hybrid method outperforms the conventional music Signal Separation methods.
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hybrid multichannel Signal Separation using supervised nonnegative matrix factorization with spectrogram restoration
Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, 2014Co-Authors: Daichi Kitamura, Hiroshi Saruwatari, Kazunobu Kondo, Yu Takahashi, Satoshi Nakamura, Hirokazu KameokaAbstract:In this paper, we propose a new hybrid method that concatenates directional clustering and advanced nonnegative matrix factorization (NMF) for the purpose of the specific sound extraction from the multichannel music Signal. Multichannel music Signal Separation technology is aimed to extract a specific target Signal from observed multichannel Signals that contain multiple instrumental sounds. In the previous studies, various methods using NMF have been proposed, but they remain many problems, e.g., poor convergence in update rules in NMF and lack of robustness. To solve these problems, we propose a new supervised NMF (SNMF) with spectrogram restoration and its hybrid method that concatenates the proposed SNMF after directional clustering. Via extrapolation of supervised spectral bases, the proposed SNMF attempts both target Signal Separation and reconstruction of the lost target components, which are generated by preceding directional clustering. In addition, we theoretically reveal the trade-off between Separation and extrapolation abilities and propose a new scheme for multi-divergence, where optimal divergence can be automatically changed in each time frame according to the local spatial conditions. The results of an evaluation experiment show that our proposed hybrid method outperforms the conventional music Signal Separation methods.
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divergence optimization in nonnegative matrix factorization with spectrogram restoration for multichannel Signal Separation
Hands-free Speech Communication and Microphone Arrays (HSCMA) 2014 4th Joint Workshop on, 2014Co-Authors: Daichi Kitamura, Hiroshi Saruwatari, Kazunobu Kondo, Yu Takahashi, Satoshi Nakamura, Hirokazu KameokaAbstract:In this paper, we address an optimization issue for the divergence in supervised nonnegative matrix factorization with spectrogram restoration, which has been proposed for addressing multichannel Signal Separation. This method separates non-target components and reconstructs some missing data caused by preceding spatial clustering via supervised basis extrapolation. In our previous study, we only used a limited type of divergence, whereas the divergence selection is essential. Therefore, we extend this method to a more generalized form and give a theoretical analysis of the divergence optimization, where we reveal the trade-off between Separation and extrapolation abilities.
Yu Takahashi - One of the best experts on this subject based on the ideXlab platform.
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music Signal Separation using supervised nmf with all pole model based discriminative basis deformation
European Signal Processing Conference, 2016Co-Authors: Hiroaki Nakajima, Nobutaka Ono, Daichi Kitamura, Hiroshi Saruwatari, Yu Takahashi, Norihiro Takamune, Shoichi Koyama, Kazunobu KondoAbstract:In this paper, we address the music Signal Separation problem and propose a new supervised nonnegative matrix factorization (SNMF) algorithm employing the deformation of a spectral supervision basis trained in advance. Conventional SNMF has a problem that the Separation accuracy is degraded by a mismatch between the trained basis and the spectrogram of the actual target sound in open data. To reduce the mismatch problem, we propose a new method with two features. First, we introduce a deformation with an all-pole model that is optimized to make the trained basis fit the spectrogram of the target Signal, even if the true target component is hidden in the observed mixture. Next, to avoid an excess deformation, we limit the degree of freedom in the deformation by performing discriminative training. Our experimental evaluation reveals that the proposed method outperforms conventional SNMFs.
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multichannel Signal Separation combining directional clustering and nonnegative matrix factorization with spectrogram restoration
IEEE Transactions on Audio Speech and Language Processing, 2015Co-Authors: Daichi Kitamura, Hiroshi Saruwatari, Kazunobu Kondo, Yu Takahashi, Hirokazu Kameoka, Satoshi NakamuraAbstract:In this paper, to address problems in multichannel music Signal Separation, we propose a new hybrid method that combines directional clustering and advanced nonnegative matrix factorization (NMF). The aims of multichannel music Signal Separation technology is to extract a specific target Signal from observed multichannel Signals that contain multiple instrumental sounds. In previous studies, various methods using NMF have been proposed, but many problems remain including poor Separation accuracy and lack of robustness. To solve these problems, we propose a new supervised NMF (SNMF) with spectrogram restoration and a hybrid method that concatenates the proposed SNMF after directional clustering. Via the extrapolation of supervised spectral bases, the proposed SNMF attempts both target Signal Separation and reconstruction of the lost target components, which are generated by preceding directional clustering. In addition, we experimentally reveal the trade-off between Separation and extrapolation abilities and propose a new scheme for adaptive divergence, where the optimal divergence can be automatically changed in each time frame according to the local spatial conditions. The results of an evaluation experiment show that our proposed hybrid method outperforms the conventional music Signal Separation methods.
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hybrid multichannel Signal Separation using supervised nonnegative matrix factorization with spectrogram restoration
Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, 2014Co-Authors: Daichi Kitamura, Hiroshi Saruwatari, Kazunobu Kondo, Yu Takahashi, Satoshi Nakamura, Hirokazu KameokaAbstract:In this paper, we propose a new hybrid method that concatenates directional clustering and advanced nonnegative matrix factorization (NMF) for the purpose of the specific sound extraction from the multichannel music Signal. Multichannel music Signal Separation technology is aimed to extract a specific target Signal from observed multichannel Signals that contain multiple instrumental sounds. In the previous studies, various methods using NMF have been proposed, but they remain many problems, e.g., poor convergence in update rules in NMF and lack of robustness. To solve these problems, we propose a new supervised NMF (SNMF) with spectrogram restoration and its hybrid method that concatenates the proposed SNMF after directional clustering. Via extrapolation of supervised spectral bases, the proposed SNMF attempts both target Signal Separation and reconstruction of the lost target components, which are generated by preceding directional clustering. In addition, we theoretically reveal the trade-off between Separation and extrapolation abilities and propose a new scheme for multi-divergence, where optimal divergence can be automatically changed in each time frame according to the local spatial conditions. The results of an evaluation experiment show that our proposed hybrid method outperforms the conventional music Signal Separation methods.
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divergence optimization in nonnegative matrix factorization with spectrogram restoration for multichannel Signal Separation
Hands-free Speech Communication and Microphone Arrays (HSCMA) 2014 4th Joint Workshop on, 2014Co-Authors: Daichi Kitamura, Hiroshi Saruwatari, Kazunobu Kondo, Yu Takahashi, Satoshi Nakamura, Hirokazu KameokaAbstract:In this paper, we address an optimization issue for the divergence in supervised nonnegative matrix factorization with spectrogram restoration, which has been proposed for addressing multichannel Signal Separation. This method separates non-target components and reconstructs some missing data caused by preceding spatial clustering via supervised basis extrapolation. In our previous study, we only used a limited type of divergence, whereas the divergence selection is essential. Therefore, we extend this method to a more generalized form and give a theoretical analysis of the divergence optimization, where we reveal the trade-off between Separation and extrapolation abilities.
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music Signal Separation based on bayesian spectral amplitude estimator with automatic target prior adaptation
International Conference on Acoustics Speech and Signal Processing, 2014Co-Authors: Yuki Murota, Daichi Kitamura, Hiroshi Saruwatari, Yu Takahashi, Shunsuke Nakai, Satoshi Nakamura, Kazunobu KondoAbstract:In this paper, we propose a new approach for addressing music Signal Separation based on the generalized Bayesian estimator with automatic prior adaptation. This method consists of three parts, namely, the generalized MMSE-STSA estimator with a flexible target Signal prior, the NMF-based dynamic interference spectrogram estimator, and closed-form parameter estimation for the statistical model of the target Signal based on higher-order statistics. The statistical model parameter of the hidden target Signal can be detected automatically for optimal Bayesian estimation with online target-Signal prior adaptation. Our experimental evaluation can show the efficacy of the proposed method.
E Weinstein - One of the best experts on this subject based on the ideXlab platform.
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Multichannel Signal Separation: methods and analysis
IEEE Transactions on Signal Processing, 1996Co-Authors: D. Yellin, E WeinsteinAbstract:The problem of multichannel Signal Separation has attracted considerable interest in recent literature. A variety of methods and criteria have been proposed to solve the problem based on statistical independence between the source Signals. Most of these criteria involve the computation of high-order statistics of the observed Signals. We present a unified framework for many of these criteria and analyze their statistical variance.
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criteria for multichannel Signal Separation
IEEE Transactions on Signal Processing, 1994Co-Authors: D. Yellin, E WeinsteinAbstract:We consider the problem in which we want to separate two (or more) Signals that are coupled to each other through an unknown multiple-input-multiple-output linear system (channel). We prove that the Signals can be decoupled, or separated, using only the condition that they are statistically independent, and find even weaker sufficient conditions involving their cross-polyspectra. By imposing these conditions on the reconstructed Signals, we obtain a class of criteria for Signal Separation. These criteria are universal in the sense that they do not require any prior knowledge or information concerning The nature of the source Signals. They may be communication Signals, or speech Signals, or any other 1-D or multidimensional Signals (e.g., images). Computationally efficient algorithms for implementing the proposed criteria, that only involve the iterative solution to a linear least squares problem, are presented. >
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multi channel Signal Separation by decorrelation
IEEE Transactions on Speech and Audio Processing, 1993Co-Authors: E Weinstein, Meir Feder, Alan V OppenheimAbstract:Identification of an unknown system and recovery of the input Signals from observations of the outputs of an unknown multiple-input, multiple-output linear system are considered. Attention is focused on the two-channel case, in which the outputs of a 2*2 linear time invariant system are observed. The approach consists of reconstructing the input Signals by assuming that they are statistically uncorrelated and imposing this constraint on the Signal estimates. In order to restrict the set of solutions, additional information on the true Signal generation and/or on the form of the coupling systems is incorporated. Specific algorithms are developed and tested. As a special case, these algorithms suggest a potentially interesting modification of Widrow's (1975) least-squares method for noise cancellation, where the reference Signal contains a component of the desired Signal. >
