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Hiroshi Saruwatari - One of the best experts on this subject based on the ideXlab platform.

  • Mutual-Information-Based Sensor Placement for Spatial Sound Field Recording
    ICASSP 2020 - 2020 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2020
    Co-Authors: Kentaro Ariga, Shoichi Koyama, Tomoya Nishida, Natsuki Ueno, Hiroshi Saruwatari
    Abstract:

    A sensor (microphone) placement method based on mutual information for spatial Sound Field recording is proposed. The Sound Field recording methods using distributed sensors enable the estimation of the Sound Field inside a target region of arbitrary shape; however, it is a difficult task to find the best placement of sensors. We focus on the mutual-information-based sensor placement method in which spatial phenomena are modeled as a Gaussian process (GP). We propose the use of the Sound-Field-interpolation kernel for the covariance of measurements in a GP model to obtain the sensor placement suitable for Sound Field recording. We also extend the method to treat broadband signals and derive an efficient algorithm based on block matrix inversion. Numerical simulation results indicated that the proposed method achieves accurate Sound Field estimation compared with a method using the generally used Gaussian kernel.

  • Gridless Sound Field Decomposition Based on Reciprocity Gap Functional in Spherical Harmonic Domain
    2018 IEEE 10th Sensor Array and Multichannel Signal Processing Workshop (SAM), 2018
    Co-Authors: Yuhta Takida, Shoichi Koyama, Natsuki Ueno, Hiroshi Saruwatari
    Abstract:

    A gridless Sound Field decomposition method based on the reciprocity gap functional (RGF) is proposed. An intuitive and powerful way of reconstructing a Sound Field inside a region including Sound sources is to decompose the Sound Field into Green's functions. Current methods based on sparse representation require discretization of the reconstruction region into grid points to construct the dictionary matrix; however, this procedure causes an off-grid problem and has a high computational cost. We apply the RGF, which was first proposed in the Field of inverse problems, to Sound Field decomposition in the spherical harmonic domain. The proposed method enables a Sound Field to be decomposed in a gridless manner with a computationally efficient algorithm. Numerical simulation results indicated that the reconstruction accuracy as well as the source localization accuracy can be improved by the proposed method compared with current methods, especially at low frequencies.

  • sparse Sound Field decomposition for super resolution in recording and reproduction
    Journal of the Acoustical Society of America, 2018
    Co-Authors: Shoichi Koyama, Naoki Murata, Hiroshi Saruwatari
    Abstract:

    A Sound Field recording and reproduction method based on sparse Sound Field decomposition is proposed. Most current methods are based on plane-wave or harmonic decomposition of the pressure distribution obtained by microphones, which leads to spatial aliasing artifacts with severe effects. This paper proposes a method for Sound Field decomposition based on a generative model of the Sound Field consisting of near-Field source components and far-Field plane-wave components. Since the distribution of the near-Field source components can be assumed to be spatially sparse, the pressure distribution obtained by the microphones can be decomposed into these two components using sparse decomposition algorithms. Using the proposed method, the Sound Field can be more accurately interpolated and super-resolution in recording and reproduction can be achieved. Experimental results show that the reproduction accuracy above the spatial Nyquist frequency determined by the microphone intervals was improved compared with that of the current methods.A Sound Field recording and reproduction method based on sparse Sound Field decomposition is proposed. Most current methods are based on plane-wave or harmonic decomposition of the pressure distribution obtained by microphones, which leads to spatial aliasing artifacts with severe effects. This paper proposes a method for Sound Field decomposition based on a generative model of the Sound Field consisting of near-Field source components and far-Field plane-wave components. Since the distribution of the near-Field source components can be assumed to be spatially sparse, the pressure distribution obtained by the microphones can be decomposed into these two components using sparse decomposition algorithms. Using the proposed method, the Sound Field can be more accurately interpolated and super-resolution in recording and reproduction can be achieved. Experimental results show that the reproduction accuracy above the spatial Nyquist frequency determined by the microphone intervals was improved compared with th...

  • Spatio-temporal sparse Sound Field decomposition considering acoustic source signal characteristics
    2017 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2017
    Co-Authors: Naoki Murata, Shoichi Koyama, Norihiro Takamune, Hiroshi Saruwatari
    Abstract:

    We propose a Sound Field decomposition method that takes into consideration spatio-temporal sparsity. It has been proved that sparse representation of a Sound Field is effective in reducing errors originating from spatial aliasing artifacts compared with conventional plane wave decomposition. In most current methods of sparse Sound Field decomposition, the spatial sparsity of the Sound source distribution is only assumed. However, it is known that the temporal structure of the source signal to be decomposed can also be sparse in the time-frequency domain. We formulate an objective function for sparse Sound Field decomposition by using the ℓp,q-norm to simultaneously induce sparsity in the space and time domains. An optimization algorithm on the auxiliary function method is derived to solve it. Numerical simulations of acoustic holography indicate that the reconstruction accuracy can be improved by controlling the parameter of temporal sparsity. We also demonstrate that a statistical measure of the source signals can be used as an indicator to determine a nearly optimal parameter.

  • Sound Field decomposition in reverberant environment using sparse and low-rank signal models
    2016 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2016
    Co-Authors: Shoichi Koyama, Hiroshi Saruwatari
    Abstract:

    A Sound Field decomposition method for a reverberant environment is proposed. Sound Field decomposition is the foundation of various acoustic signal processing applications and enables the estimation of the entire Sound Field from pressure measurements. Although spatial Fourier analysis of the Sound Field has been widely used, sparse decomposition of the Sound Field has recently been proved to be effective in several applications. However, in current methods, no constraints are imposed on ambiance components, whereas source components are assumed to be sparsely distributed in the space. This results in inaccurate decomposition in a reverberant environment. The proposed method is based on sparse and low-rank signal models, which are used for simultaneous decomposition of the observed signals into source and ambiance components. Numerical simulation results indicated that the decomposition accuracy is superior to that of current methods.

Shoichi Koyama - One of the best experts on this subject based on the ideXlab platform.

  • Mutual-Information-Based Sensor Placement for Spatial Sound Field Recording
    ICASSP 2020 - 2020 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2020
    Co-Authors: Kentaro Ariga, Shoichi Koyama, Tomoya Nishida, Natsuki Ueno, Hiroshi Saruwatari
    Abstract:

    A sensor (microphone) placement method based on mutual information for spatial Sound Field recording is proposed. The Sound Field recording methods using distributed sensors enable the estimation of the Sound Field inside a target region of arbitrary shape; however, it is a difficult task to find the best placement of sensors. We focus on the mutual-information-based sensor placement method in which spatial phenomena are modeled as a Gaussian process (GP). We propose the use of the Sound-Field-interpolation kernel for the covariance of measurements in a GP model to obtain the sensor placement suitable for Sound Field recording. We also extend the method to treat broadband signals and derive an efficient algorithm based on block matrix inversion. Numerical simulation results indicated that the proposed method achieves accurate Sound Field estimation compared with a method using the generally used Gaussian kernel.

  • Gridless Sound Field Decomposition Based on Reciprocity Gap Functional in Spherical Harmonic Domain
    2018 IEEE 10th Sensor Array and Multichannel Signal Processing Workshop (SAM), 2018
    Co-Authors: Yuhta Takida, Shoichi Koyama, Natsuki Ueno, Hiroshi Saruwatari
    Abstract:

    A gridless Sound Field decomposition method based on the reciprocity gap functional (RGF) is proposed. An intuitive and powerful way of reconstructing a Sound Field inside a region including Sound sources is to decompose the Sound Field into Green's functions. Current methods based on sparse representation require discretization of the reconstruction region into grid points to construct the dictionary matrix; however, this procedure causes an off-grid problem and has a high computational cost. We apply the RGF, which was first proposed in the Field of inverse problems, to Sound Field decomposition in the spherical harmonic domain. The proposed method enables a Sound Field to be decomposed in a gridless manner with a computationally efficient algorithm. Numerical simulation results indicated that the reconstruction accuracy as well as the source localization accuracy can be improved by the proposed method compared with current methods, especially at low frequencies.

  • sparse Sound Field decomposition for super resolution in recording and reproduction
    Journal of the Acoustical Society of America, 2018
    Co-Authors: Shoichi Koyama, Naoki Murata, Hiroshi Saruwatari
    Abstract:

    A Sound Field recording and reproduction method based on sparse Sound Field decomposition is proposed. Most current methods are based on plane-wave or harmonic decomposition of the pressure distribution obtained by microphones, which leads to spatial aliasing artifacts with severe effects. This paper proposes a method for Sound Field decomposition based on a generative model of the Sound Field consisting of near-Field source components and far-Field plane-wave components. Since the distribution of the near-Field source components can be assumed to be spatially sparse, the pressure distribution obtained by the microphones can be decomposed into these two components using sparse decomposition algorithms. Using the proposed method, the Sound Field can be more accurately interpolated and super-resolution in recording and reproduction can be achieved. Experimental results show that the reproduction accuracy above the spatial Nyquist frequency determined by the microphone intervals was improved compared with that of the current methods.A Sound Field recording and reproduction method based on sparse Sound Field decomposition is proposed. Most current methods are based on plane-wave or harmonic decomposition of the pressure distribution obtained by microphones, which leads to spatial aliasing artifacts with severe effects. This paper proposes a method for Sound Field decomposition based on a generative model of the Sound Field consisting of near-Field source components and far-Field plane-wave components. Since the distribution of the near-Field source components can be assumed to be spatially sparse, the pressure distribution obtained by the microphones can be decomposed into these two components using sparse decomposition algorithms. Using the proposed method, the Sound Field can be more accurately interpolated and super-resolution in recording and reproduction can be achieved. Experimental results show that the reproduction accuracy above the spatial Nyquist frequency determined by the microphone intervals was improved compared with th...

  • Spatio-temporal sparse Sound Field decomposition considering acoustic source signal characteristics
    2017 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2017
    Co-Authors: Naoki Murata, Shoichi Koyama, Norihiro Takamune, Hiroshi Saruwatari
    Abstract:

    We propose a Sound Field decomposition method that takes into consideration spatio-temporal sparsity. It has been proved that sparse representation of a Sound Field is effective in reducing errors originating from spatial aliasing artifacts compared with conventional plane wave decomposition. In most current methods of sparse Sound Field decomposition, the spatial sparsity of the Sound source distribution is only assumed. However, it is known that the temporal structure of the source signal to be decomposed can also be sparse in the time-frequency domain. We formulate an objective function for sparse Sound Field decomposition by using the ℓp,q-norm to simultaneously induce sparsity in the space and time domains. An optimization algorithm on the auxiliary function method is derived to solve it. Numerical simulations of acoustic holography indicate that the reconstruction accuracy can be improved by controlling the parameter of temporal sparsity. We also demonstrate that a statistical measure of the source signals can be used as an indicator to determine a nearly optimal parameter.

  • Sound Field decomposition in reverberant environment using sparse and low-rank signal models
    2016 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2016
    Co-Authors: Shoichi Koyama, Hiroshi Saruwatari
    Abstract:

    A Sound Field decomposition method for a reverberant environment is proposed. Sound Field decomposition is the foundation of various acoustic signal processing applications and enables the estimation of the entire Sound Field from pressure measurements. Although spatial Fourier analysis of the Sound Field has been widely used, sparse decomposition of the Sound Field has recently been proved to be effective in several applications. However, in current methods, no constraints are imposed on ambiance components, whereas source components are assumed to be sparsely distributed in the space. This results in inaccurate decomposition in a reverberant environment. The proposed method is based on sparse and low-rank signal models, which are used for simultaneous decomposition of the observed signals into source and ambiance components. Numerical simulation results indicated that the decomposition accuracy is superior to that of current methods.

Yasuhiro Oikawa - One of the best experts on this subject based on the ideXlab platform.

  • Effect of Switching Reproduction Area in Dynamic Local Sound Field Synthesis
    2019 IEEE 8th Global Conference on Consumer Electronics (GCCE), 2019
    Co-Authors: Kakeru Kurokawa, Yusuke Ikeda, Izumi Tsunokuni, Naotoshi Osaka, Yasuhiro Oikawa
    Abstract:

    Many physical Sound Field synthesis techniques have been studied. To implement these techniques, a large-scale reproduction system is usually required with a large number of loudspeakers, D/A converters, and amplifiers. Thus, the techniques are not suitable for use in a rooms of an ordinary house. In our recent studies, a physical Sound Field reproduction system was developed with a digital loudspeaker array and suitable for an ordinary, small room. To more accurately reproduce the Sound Field with the implemented loudspeakers, an effective method is local Sound Field synthesis (LSFS). Because the area of the reproduced Sound Field is limited in LSFS, the reproduction area must track the listener's head movement. To track the listener's head movement, the driving functions of loudspeakers must be changed in real time. In this study, the effect of switching the reproduction area is investigated to determine the appropriate switching rate and size of the local area.

  • PDE-based interpolation method for optically visualized Sound Field
    2014 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2014
    Co-Authors: Kohei Yatabe, Yasuhiro Oikawa
    Abstract:

    An effective way to understand the behavior of a Sound Field is to visualize it. An optical measurement method is a suitable option for this as it enables contactless non-destructive measurement. After measuring a Sound Field, interpolation of the data is necessary for a smooth visualization. However, conventional interpolation methods cannot provide a physically meaningful result especially when the condition of the measurement causes moiré effect. In this paper, a special interpolation method for an optically visualized Sound Field based on the Kirchhoff-Helmholtz integral equation is proposed.

  • Extraction of Sound Field information from flowing dust captured with high-speed camera
    2012 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2012
    Co-Authors: Mariko Akutsu, Yasuhiro Oikawa
    Abstract:

    In this paper, we propose a measuring method of the Sound Field from high-speed movie of dust. The movements of dust in the Sound Field are affected by the Sound vibration. We observe the dust using high-speed cameras. The movie is recorded by one high-speed camera in order to get the information of 2-D Sound Field and two high-speed cameras for 3-D. The influence of air current is reduced from the movement of dust so that the Sound Field information is extracted. The experimental results indicate that this method is effective to observe the Sound Field especially composed of low frequency components.

  • Sound Field measurements based on reconstruction from laser projections
    ICASSP IEEE International Conference on Acoustics Speech and Signal Processing - Proceedings, 2005
    Co-Authors: Yasuhiro Oikawa, Yusuke Ikeda, Toshikazu Takizawa, Makoto Goto, Yoshio Yamasaki
    Abstract:

    In this paper, we describe some new Sound Field measurement methods by using a laser Doppler vibrometer (LDV). By irradiating the reflection wall with a laser, we can observe the light velocity change that is caused by the refractive index change from the change in air density. It means that it is possible to observe the change of the Sound pressure. We measured a Sound Field projection on a 2D plane using a scanning laser Doppler vibrometer (SVM) which can visualize a Sound Field. And we made a 3D Sound Field reconstruction from some 2D laser projections based on computed tomography (CT) techniques. We made the reconstructed image for the Sound Field near the loudspeaker or in the room.

Naoki Murata - One of the best experts on this subject based on the ideXlab platform.

  • sparse Sound Field decomposition for super resolution in recording and reproduction
    Journal of the Acoustical Society of America, 2018
    Co-Authors: Shoichi Koyama, Naoki Murata, Hiroshi Saruwatari
    Abstract:

    A Sound Field recording and reproduction method based on sparse Sound Field decomposition is proposed. Most current methods are based on plane-wave or harmonic decomposition of the pressure distribution obtained by microphones, which leads to spatial aliasing artifacts with severe effects. This paper proposes a method for Sound Field decomposition based on a generative model of the Sound Field consisting of near-Field source components and far-Field plane-wave components. Since the distribution of the near-Field source components can be assumed to be spatially sparse, the pressure distribution obtained by the microphones can be decomposed into these two components using sparse decomposition algorithms. Using the proposed method, the Sound Field can be more accurately interpolated and super-resolution in recording and reproduction can be achieved. Experimental results show that the reproduction accuracy above the spatial Nyquist frequency determined by the microphone intervals was improved compared with that of the current methods.A Sound Field recording and reproduction method based on sparse Sound Field decomposition is proposed. Most current methods are based on plane-wave or harmonic decomposition of the pressure distribution obtained by microphones, which leads to spatial aliasing artifacts with severe effects. This paper proposes a method for Sound Field decomposition based on a generative model of the Sound Field consisting of near-Field source components and far-Field plane-wave components. Since the distribution of the near-Field source components can be assumed to be spatially sparse, the pressure distribution obtained by the microphones can be decomposed into these two components using sparse decomposition algorithms. Using the proposed method, the Sound Field can be more accurately interpolated and super-resolution in recording and reproduction can be achieved. Experimental results show that the reproduction accuracy above the spatial Nyquist frequency determined by the microphone intervals was improved compared with th...

  • Spatio-temporal sparse Sound Field decomposition considering acoustic source signal characteristics
    2017 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2017
    Co-Authors: Naoki Murata, Shoichi Koyama, Norihiro Takamune, Hiroshi Saruwatari
    Abstract:

    We propose a Sound Field decomposition method that takes into consideration spatio-temporal sparsity. It has been proved that sparse representation of a Sound Field is effective in reducing errors originating from spatial aliasing artifacts compared with conventional plane wave decomposition. In most current methods of sparse Sound Field decomposition, the spatial sparsity of the Sound source distribution is only assumed. However, it is known that the temporal structure of the source signal to be decomposed can also be sparse in the time-frequency domain. We formulate an objective function for sparse Sound Field decomposition by using the ℓp,q-norm to simultaneously induce sparsity in the space and time domains. An optimization algorithm on the auxiliary function method is derived to solve it. Numerical simulations of acoustic holography indicate that the reconstruction accuracy can be improved by controlling the parameter of temporal sparsity. We also demonstrate that a statistical measure of the source signals can be used as an indicator to determine a nearly optimal parameter.

  • Sparse Sound Field decomposition using group sparse Bayesian learning
    2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2015
    Co-Authors: Shoichi Koyama, Atsushi Matsubayashi, Naoki Murata, Hiroshi Saruwatari
    Abstract:

    A sparse Sound Field decomposition method is proposed. Sound Field decomposition is the foundation of the various acoustic signal processing applications and enables the estimation of the entire Sound Field from pressure measurements. The plane wave decomposition, i.e., spatial Fourier analysis, of the Sound Field has been widely used; however, artifacts originating from spatial aliasing occur above the spatial Nyquist frequency. We have proposed a sparse Sound Field decomposition method based on a generative model as a sum of monopole source and plane wave components in the context of Sound Field recording and reproduction. For more accurate and robust decomposition, we propose three different group sparse signal models based on physical properties and a decomposition algorithm by extending sparse Bayesian learning. In simulation experiments, the accuracy of sparse decomposition was improved compared with that of current methods.

N. Epain - One of the best experts on this subject based on the ideXlab platform.

  • Super-resolution Sound Field imaging with sub-space pre-processing
    2013 IEEE International Conference on Acoustics Speech and Signal Processing, 2013
    Co-Authors: N. Epain
    Abstract:

    Spherical microphone arrays are a powerful tool for Sound Field analysis. In previous work, we have shown that sparse recovery can be used to arbitrarily increase the resolution of the Sound Field recorded by a spherical microphone array. Because these super-resolution techniques rely on the assumption that the Sound Field results from a few dominant plane waves, they are not robust to the presence of noise or reverberation. In this paper we propose a simple method to separate the Sound Field into a directional component and a diffuse component prior to applying sparse recovery techniques. Simulations show that this pre-processing could dramatically improve the results of sparse recovery in noisy or reverberant environments.