The Experts below are selected from a list of 324 Experts worldwide ranked by ideXlab platform
Shoji Makino - One of the best experts on this subject based on the ideXlab platform.
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EUSIPCO - Normalized Observation Vector clustering approach for sparse source separation
2006Co-Authors: Shoko Araki, Hiroshi Sawada, Ryo Mukai, Shoji MakinoAbstract:This paper presents a new method for the blind separation of sparse sources whose number N can exceed the number of sensors M. Recently, sparseness based blind separation has been actively studied. However, most methods utilize a linear sensor array (or only two sensors), and therefore have certain limitations; e.g., they cannot be applied to symmetrically positioned sources. To allow the use of more than two sensors that can be arranged in a non-linear/non-uniform way, we propose a new method that includes the normalization and clustering of the Observation Vectors. We report promising results for the speech separation of 3-dimensionally distributed five sources with a non-linear/non-uniform array of four sensors in a room (RT 60 = 120 ms).
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Underdetermined sparse source separation of convolutive mixtures with Observation Vector clustering
2006 IEEE International Symposium on Circuits and Systems, 2006Co-Authors: S. Araki, Hiroshi Sawada, Ryo Mukai, Shoji MakinoAbstract:We propose a new method for solving the underdetermined sparse signal separation problem. Some sparseness based methods have already been proposed. However, most of these methods utilized a linear sensor array (or only two sensors), and therefore they have certain limitations; e.g., they cannot separate symmetrically positioned sources. To allow the use of more than three sensors that can be arranged in a non-linear/non-uniform way, we propose a new method that includes the normalization and clustering of the Observation Vectors. Our proposed method can handle both underdetermined case and (over-)determined cases. We show practical results for speech separation with non-linear/non-uniform sensor arrangements. We obtained promising experimental results for the cases of 3 times 4, 4 times 5 (#sensors times #sources) in a room (RT60= 120 ms)
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ISCAS - Underdetermined sparse source separation of convolutive mixtures with Observation Vector clustering
2006 IEEE International Symposium on Circuits and Systems, 2006Co-Authors: Shoko Araki, Hiroshi Sawada, Ryo Mukai, Shoji MakinoAbstract:We propose a new method for solving the underdetermined sparse signal separation problem. Some sparseness based methods have already been proposed. However, most of these methods utilized a linear sensor array (or only two sensors), and therefore they have certain limitations; e.g., they cannot separate symmetrically positioned sources. To allow the use of more than three sensors that can be arranged in a non-linear/non-uniform way, we propose a new method that includes the normalization and clustering of the Observation Vectors. Our proposed method can handle both underdetermined case and (over-)determined cases. We show practical results for speech separation with non-linear/non-uniform sensor arrangements. We obtained promising experimental results for the cases of 3 /spl times/ 4, 4 /spl times/ 5 (#sensors /spl times/ #sources) in a room (RT/sub 60/= 120 ms).
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Doa Estimation for Multiple Sparse Sources with Normalized Observation Vector Clustering
2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, 2006Co-Authors: S. Araki, Hiroshi Sawada, Ryo Mukai, Shoji MakinoAbstract:This paper presents a new method for estimating the direction of arrival (DOA) of source signals whose number N can exceed the number of sensors M. Subspace based methods, e.g., the MUSIC algorithm, have been widely studied, however, they are only applicable when M > N. Another conventional independent component analysis based method allows M ges N, however, it cannot be applied when M < N. By contrast, our new method can be applied where the sources outnumber the sensors (i.e., an underdetermined case M < N) by assuming source sparseness. Our method can cope with 2- or 3-dimensionally distributed sources with a 2- or 3-dimensional sensor array. We obtained promising experimental results for 3 times 4, 3 times 5 and 4 times 5 (#sensors times #speech sources) in a room (RT60= 120 ms)
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Normalized Observation Vector clustering approach for sparse source separation
2006 14th European Signal Processing Conference, 2006Co-Authors: Shoko Araki, Hiroshi Sawada, Ryo Mukai, Shoji MakinoAbstract:This paper presents a new method for the blind separation of sparse sources whose number N can exceed the number of sensors M. Recently, sparseness based blind separation has been actively studied. However, most methods utilize a linear sensor array (or only two sensors), and therefore have certain limitations; e.g., they cannot be applied to symmetrically positioned sources. To allow the use of more than two sensors that can be arranged in a non-linear/non-uniform way, we propose a new method that includes the normalization and clustering of the Observation Vectors. We report promising results for the speech separation of 3-dimensionally distributed five sources with a non-linear/non-uniform array of four sensors in a room (RT60= 120 ms).
Shoko Araki - One of the best experts on this subject based on the ideXlab platform.
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Recent Advances in Multichannel Source Separation and Denoising Based on Source Sparseness
Audio Source Separation, 2018Co-Authors: Shoko Araki, Tomohiro NakataniAbstract:This chapter deals with multichannel source separation and denoising based on sparseness of source signals in the time-frequency domain. In this approach, time-frequency masks are typically estimated based on clustering of source location features, such as time and level differences between microphones. In this chapter, we describe the approach and its recent advances. Especially, we introduce a recently proposed clustering method, Observation Vector clustering, which has attracted attention for its effectiveness. We introduce algorithms for Observation Vector clustering based on a complex Watson mixture model (cWMM), a complex Bingham mixture model (cBMM), and a complex Gaussian mixture model (cGMM). We show through experiments the effectiveness of Observation Vector clustering in source separation and denoising.
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Spatial correlation model based Observation Vector clustering and MVDR beamforming for meeting recognition
2016 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2016Co-Authors: Shoko Araki, Masahiro Okada, Takuya Higuchi, Atsunori Ogawa, Tomohiro NakataniAbstract:This paper addresses a minimum variance distortionless response (MVDR) beamforming based speech enhancement approach for meeting speech recognition. In a meeting situation, speaker overlaps and noise signals are not negligible. To handle these issues, we employ MVDR beamforming, where accurate estimation of the steering Vector is paramount. We recently found that steering Vector estimation by clustering the time-frequency components of microphone Observation Vectors performs well as regards real-world noise reduction. The clustering is performed by taking a cue from the spatial correlation matrix of each speaker, which is realized by modeling the time-frequency components of the Observation Vectors with a complex Gaussian mixture model (CGMM). Experimental results with real recordings show that the proposed MVDR scheme outperforms conventional null-beamformer based speech enhancement in a meeting situation.
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ICASSP - Spatial correlation model based Observation Vector clustering and MVDR beamforming for meeting recognition
2016 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2016Co-Authors: Shoko Araki, Masahiro Okada, Takuya Higuchi, Atsunori Ogawa, Tomohiro NakataniAbstract:This paper addresses a minimum variance distortionless response (MVDR) beamforming based speech enhancement approach for meeting speech recognition. In a meeting situation, speaker overlaps and noise signals are not negligible. To handle these issues, we employ MVDR beamforming, where accurate estimation of the steering Vector is paramount. We recently found that steering Vector estimation by clustering the time-frequency components of microphone Observation Vectors performs well as regards real-world noise reduction. The clustering is performed by taking a cue from the spatial correlation matrix of each speaker, which is realized by modeling the time-frequency components ofthe Observation Vectors with a complex Gaussian mixture model (CGMM). Experimental results with real recordings show that the proposed MVDR scheme outperforms conventional null-beamformer based speech enhancement in a meeting situation.
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EUSIPCO - Normalized Observation Vector clustering approach for sparse source separation
2006Co-Authors: Shoko Araki, Hiroshi Sawada, Ryo Mukai, Shoji MakinoAbstract:This paper presents a new method for the blind separation of sparse sources whose number N can exceed the number of sensors M. Recently, sparseness based blind separation has been actively studied. However, most methods utilize a linear sensor array (or only two sensors), and therefore have certain limitations; e.g., they cannot be applied to symmetrically positioned sources. To allow the use of more than two sensors that can be arranged in a non-linear/non-uniform way, we propose a new method that includes the normalization and clustering of the Observation Vectors. We report promising results for the speech separation of 3-dimensionally distributed five sources with a non-linear/non-uniform array of four sensors in a room (RT 60 = 120 ms).
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ISCAS - Underdetermined sparse source separation of convolutive mixtures with Observation Vector clustering
2006 IEEE International Symposium on Circuits and Systems, 2006Co-Authors: Shoko Araki, Hiroshi Sawada, Ryo Mukai, Shoji MakinoAbstract:We propose a new method for solving the underdetermined sparse signal separation problem. Some sparseness based methods have already been proposed. However, most of these methods utilized a linear sensor array (or only two sensors), and therefore they have certain limitations; e.g., they cannot separate symmetrically positioned sources. To allow the use of more than three sensors that can be arranged in a non-linear/non-uniform way, we propose a new method that includes the normalization and clustering of the Observation Vectors. Our proposed method can handle both underdetermined case and (over-)determined cases. We show practical results for speech separation with non-linear/non-uniform sensor arrangements. We obtained promising experimental results for the cases of 3 /spl times/ 4, 4 /spl times/ 5 (#sensors /spl times/ #sources) in a room (RT/sub 60/= 120 ms).
Ryo Mukai - One of the best experts on this subject based on the ideXlab platform.
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EUSIPCO - Normalized Observation Vector clustering approach for sparse source separation
2006Co-Authors: Shoko Araki, Hiroshi Sawada, Ryo Mukai, Shoji MakinoAbstract:This paper presents a new method for the blind separation of sparse sources whose number N can exceed the number of sensors M. Recently, sparseness based blind separation has been actively studied. However, most methods utilize a linear sensor array (or only two sensors), and therefore have certain limitations; e.g., they cannot be applied to symmetrically positioned sources. To allow the use of more than two sensors that can be arranged in a non-linear/non-uniform way, we propose a new method that includes the normalization and clustering of the Observation Vectors. We report promising results for the speech separation of 3-dimensionally distributed five sources with a non-linear/non-uniform array of four sensors in a room (RT 60 = 120 ms).
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Underdetermined sparse source separation of convolutive mixtures with Observation Vector clustering
2006 IEEE International Symposium on Circuits and Systems, 2006Co-Authors: S. Araki, Hiroshi Sawada, Ryo Mukai, Shoji MakinoAbstract:We propose a new method for solving the underdetermined sparse signal separation problem. Some sparseness based methods have already been proposed. However, most of these methods utilized a linear sensor array (or only two sensors), and therefore they have certain limitations; e.g., they cannot separate symmetrically positioned sources. To allow the use of more than three sensors that can be arranged in a non-linear/non-uniform way, we propose a new method that includes the normalization and clustering of the Observation Vectors. Our proposed method can handle both underdetermined case and (over-)determined cases. We show practical results for speech separation with non-linear/non-uniform sensor arrangements. We obtained promising experimental results for the cases of 3 times 4, 4 times 5 (#sensors times #sources) in a room (RT60= 120 ms)
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ISCAS - Underdetermined sparse source separation of convolutive mixtures with Observation Vector clustering
2006 IEEE International Symposium on Circuits and Systems, 2006Co-Authors: Shoko Araki, Hiroshi Sawada, Ryo Mukai, Shoji MakinoAbstract:We propose a new method for solving the underdetermined sparse signal separation problem. Some sparseness based methods have already been proposed. However, most of these methods utilized a linear sensor array (or only two sensors), and therefore they have certain limitations; e.g., they cannot separate symmetrically positioned sources. To allow the use of more than three sensors that can be arranged in a non-linear/non-uniform way, we propose a new method that includes the normalization and clustering of the Observation Vectors. Our proposed method can handle both underdetermined case and (over-)determined cases. We show practical results for speech separation with non-linear/non-uniform sensor arrangements. We obtained promising experimental results for the cases of 3 /spl times/ 4, 4 /spl times/ 5 (#sensors /spl times/ #sources) in a room (RT/sub 60/= 120 ms).
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Doa Estimation for Multiple Sparse Sources with Normalized Observation Vector Clustering
2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, 2006Co-Authors: S. Araki, Hiroshi Sawada, Ryo Mukai, Shoji MakinoAbstract:This paper presents a new method for estimating the direction of arrival (DOA) of source signals whose number N can exceed the number of sensors M. Subspace based methods, e.g., the MUSIC algorithm, have been widely studied, however, they are only applicable when M > N. Another conventional independent component analysis based method allows M ges N, however, it cannot be applied when M < N. By contrast, our new method can be applied where the sources outnumber the sensors (i.e., an underdetermined case M < N) by assuming source sparseness. Our method can cope with 2- or 3-dimensionally distributed sources with a 2- or 3-dimensional sensor array. We obtained promising experimental results for 3 times 4, 3 times 5 and 4 times 5 (#sensors times #speech sources) in a room (RT60= 120 ms)
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Normalized Observation Vector clustering approach for sparse source separation
2006 14th European Signal Processing Conference, 2006Co-Authors: Shoko Araki, Hiroshi Sawada, Ryo Mukai, Shoji MakinoAbstract:This paper presents a new method for the blind separation of sparse sources whose number N can exceed the number of sensors M. Recently, sparseness based blind separation has been actively studied. However, most methods utilize a linear sensor array (or only two sensors), and therefore have certain limitations; e.g., they cannot be applied to symmetrically positioned sources. To allow the use of more than two sensors that can be arranged in a non-linear/non-uniform way, we propose a new method that includes the normalization and clustering of the Observation Vectors. We report promising results for the speech separation of 3-dimensionally distributed five sources with a non-linear/non-uniform array of four sensors in a room (RT60= 120 ms).
Hiroshi Sawada - One of the best experts on this subject based on the ideXlab platform.
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EUSIPCO - Normalized Observation Vector clustering approach for sparse source separation
2006Co-Authors: Shoko Araki, Hiroshi Sawada, Ryo Mukai, Shoji MakinoAbstract:This paper presents a new method for the blind separation of sparse sources whose number N can exceed the number of sensors M. Recently, sparseness based blind separation has been actively studied. However, most methods utilize a linear sensor array (or only two sensors), and therefore have certain limitations; e.g., they cannot be applied to symmetrically positioned sources. To allow the use of more than two sensors that can be arranged in a non-linear/non-uniform way, we propose a new method that includes the normalization and clustering of the Observation Vectors. We report promising results for the speech separation of 3-dimensionally distributed five sources with a non-linear/non-uniform array of four sensors in a room (RT 60 = 120 ms).
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Underdetermined sparse source separation of convolutive mixtures with Observation Vector clustering
2006 IEEE International Symposium on Circuits and Systems, 2006Co-Authors: S. Araki, Hiroshi Sawada, Ryo Mukai, Shoji MakinoAbstract:We propose a new method for solving the underdetermined sparse signal separation problem. Some sparseness based methods have already been proposed. However, most of these methods utilized a linear sensor array (or only two sensors), and therefore they have certain limitations; e.g., they cannot separate symmetrically positioned sources. To allow the use of more than three sensors that can be arranged in a non-linear/non-uniform way, we propose a new method that includes the normalization and clustering of the Observation Vectors. Our proposed method can handle both underdetermined case and (over-)determined cases. We show practical results for speech separation with non-linear/non-uniform sensor arrangements. We obtained promising experimental results for the cases of 3 times 4, 4 times 5 (#sensors times #sources) in a room (RT60= 120 ms)
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ISCAS - Underdetermined sparse source separation of convolutive mixtures with Observation Vector clustering
2006 IEEE International Symposium on Circuits and Systems, 2006Co-Authors: Shoko Araki, Hiroshi Sawada, Ryo Mukai, Shoji MakinoAbstract:We propose a new method for solving the underdetermined sparse signal separation problem. Some sparseness based methods have already been proposed. However, most of these methods utilized a linear sensor array (or only two sensors), and therefore they have certain limitations; e.g., they cannot separate symmetrically positioned sources. To allow the use of more than three sensors that can be arranged in a non-linear/non-uniform way, we propose a new method that includes the normalization and clustering of the Observation Vectors. Our proposed method can handle both underdetermined case and (over-)determined cases. We show practical results for speech separation with non-linear/non-uniform sensor arrangements. We obtained promising experimental results for the cases of 3 /spl times/ 4, 4 /spl times/ 5 (#sensors /spl times/ #sources) in a room (RT/sub 60/= 120 ms).
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Doa Estimation for Multiple Sparse Sources with Normalized Observation Vector Clustering
2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, 2006Co-Authors: S. Araki, Hiroshi Sawada, Ryo Mukai, Shoji MakinoAbstract:This paper presents a new method for estimating the direction of arrival (DOA) of source signals whose number N can exceed the number of sensors M. Subspace based methods, e.g., the MUSIC algorithm, have been widely studied, however, they are only applicable when M > N. Another conventional independent component analysis based method allows M ges N, however, it cannot be applied when M < N. By contrast, our new method can be applied where the sources outnumber the sensors (i.e., an underdetermined case M < N) by assuming source sparseness. Our method can cope with 2- or 3-dimensionally distributed sources with a 2- or 3-dimensional sensor array. We obtained promising experimental results for 3 times 4, 3 times 5 and 4 times 5 (#sensors times #speech sources) in a room (RT60= 120 ms)
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Normalized Observation Vector clustering approach for sparse source separation
2006 14th European Signal Processing Conference, 2006Co-Authors: Shoko Araki, Hiroshi Sawada, Ryo Mukai, Shoji MakinoAbstract:This paper presents a new method for the blind separation of sparse sources whose number N can exceed the number of sensors M. Recently, sparseness based blind separation has been actively studied. However, most methods utilize a linear sensor array (or only two sensors), and therefore have certain limitations; e.g., they cannot be applied to symmetrically positioned sources. To allow the use of more than two sensors that can be arranged in a non-linear/non-uniform way, we propose a new method that includes the normalization and clustering of the Observation Vectors. We report promising results for the speech separation of 3-dimensionally distributed five sources with a non-linear/non-uniform array of four sensors in a room (RT60= 120 ms).
Tomohiro Nakatani - One of the best experts on this subject based on the ideXlab platform.
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Recent Advances in Multichannel Source Separation and Denoising Based on Source Sparseness
Audio Source Separation, 2018Co-Authors: Shoko Araki, Tomohiro NakataniAbstract:This chapter deals with multichannel source separation and denoising based on sparseness of source signals in the time-frequency domain. In this approach, time-frequency masks are typically estimated based on clustering of source location features, such as time and level differences between microphones. In this chapter, we describe the approach and its recent advances. Especially, we introduce a recently proposed clustering method, Observation Vector clustering, which has attracted attention for its effectiveness. We introduce algorithms for Observation Vector clustering based on a complex Watson mixture model (cWMM), a complex Bingham mixture model (cBMM), and a complex Gaussian mixture model (cGMM). We show through experiments the effectiveness of Observation Vector clustering in source separation and denoising.
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Spatial correlation model based Observation Vector clustering and MVDR beamforming for meeting recognition
2016 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2016Co-Authors: Shoko Araki, Masahiro Okada, Takuya Higuchi, Atsunori Ogawa, Tomohiro NakataniAbstract:This paper addresses a minimum variance distortionless response (MVDR) beamforming based speech enhancement approach for meeting speech recognition. In a meeting situation, speaker overlaps and noise signals are not negligible. To handle these issues, we employ MVDR beamforming, where accurate estimation of the steering Vector is paramount. We recently found that steering Vector estimation by clustering the time-frequency components of microphone Observation Vectors performs well as regards real-world noise reduction. The clustering is performed by taking a cue from the spatial correlation matrix of each speaker, which is realized by modeling the time-frequency components of the Observation Vectors with a complex Gaussian mixture model (CGMM). Experimental results with real recordings show that the proposed MVDR scheme outperforms conventional null-beamformer based speech enhancement in a meeting situation.
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ICASSP - Spatial correlation model based Observation Vector clustering and MVDR beamforming for meeting recognition
2016 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2016Co-Authors: Shoko Araki, Masahiro Okada, Takuya Higuchi, Atsunori Ogawa, Tomohiro NakataniAbstract:This paper addresses a minimum variance distortionless response (MVDR) beamforming based speech enhancement approach for meeting speech recognition. In a meeting situation, speaker overlaps and noise signals are not negligible. To handle these issues, we employ MVDR beamforming, where accurate estimation of the steering Vector is paramount. We recently found that steering Vector estimation by clustering the time-frequency components of microphone Observation Vectors performs well as regards real-world noise reduction. The clustering is performed by taking a cue from the spatial correlation matrix of each speaker, which is realized by modeling the time-frequency components ofthe Observation Vectors with a complex Gaussian mixture model (CGMM). Experimental results with real recordings show that the proposed MVDR scheme outperforms conventional null-beamformer based speech enhancement in a meeting situation.
