Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Koyama ASA ASJ joint meeting 2016

5,823 views

Published on

Shoichi Koyama, Naoki Murata, and Hiroshi Saruwatari. "Super-resolution in sound field recording and reproduction based on sparse representation"
presented at 5th Joint Meeting Acoustical Society of America and Acoustical Society of Japan (28 Nov. - 2 Dec. 2016, Honolulu, USA)

Published in: Science
  • Be the first to comment

  • Be the first to like this

Koyama ASA ASJ joint meeting 2016

  1. 1. Super-resolution in sound field recording and reproduction based on sparse representation Shoichi Koyama1,2, Naoki Murata1, and Hiroshi Saruwatari1 1The University of Tokyo 2Paris Diderot University / Institute Langevin
  2. 2. November 29, 2016 Sound field reproduction for audio system Microphone array Loudspeaker array  Large listening area can be achieved  Listeners can perceive source distance  Real-time recording and reproduction can be achieved without recording engineers Recording area Target area
  3. 3. November 29, 2016 Sound field reproduction for audio system Microphone array Loudspeaker array Telecommunication system NW Home Theatre Live broadcasting Applications Recording area Target area
  4. 4. November 29, 2016 Sound field reproduction for audio system Microphone array Loudspeaker array Improve reproduction accuracy when # of array elements is small  # of microphones > # of loudspeakers – Higher reproduction accuracy within local region of target area  # of microphones < # of loudspeakers – Higher reproduction accuracy of sources in local region of recording area [Koyama+ IEEE JSTSP2015], [Koyama+ ICASSP 2014, 2015] [Ahrens+ AES Conv. 2010], [Ueno+ ICASSP 2017 (submitted)] Recording area Target area
  5. 5. Sound Field Recording and Reproduction November 29, 2016 Recording area Target area Obtain driving signals of secondary sources (= loudspeakers) arranged on to reconstruct desired sound field inside  Inherently, sound pressure and its gradient on is required to obtain , but sound pressure is usually only known  Signal conversion for sound field recording and reproduction with ordinary acoustic sensors and transducers is necessary Primary sources
  6. 6. November 29, 2016 Conventional: WFR filtering method Recording area Target area Secondary source planeReceiving plane Primary sources Signal conversion [Koyama+ IEEE TASLP 2013] Received signals Driving signals Plane wave Plane wave Each plane wave determines entire sound field Signal conversion can be achieved in spatial frequency domain
  7. 7. November 29, 2016 Conventional: WFR filtering method Target area Received signals Driving signals Plane wave Plane wave Each plane wave determines entire sound field Spatial aliasing artifacts due to plane wave decomposition Significant error at high freq even when microphone < loudspeaker Recording area Signal conversion Secondary source planeReceiving plane Primary sources [Koyama+ IEEE TASLP 2013]
  8. 8. Sound field representation for super-resolution  Plane wave decomposition suffers from spatial aliasing artifacts because many basis functions are used  Observed signals should be represented by a few basis functions for accurate interpolation of sound field  Appropriate basis function may be close to pressure distribution originating from sound sources  To obtain driving signals of loudspeakers, basis functions must be fundamental solutions of Helmholtz equation (e.g. Green functions) November 29, 2016 Basis functionReceived signals Sound field decomposition into fundamental solutions of Helmholtz equation is necessary Sound field decomposition
  9. 9. Generative model of sound field  Inhomogeneous and homogeneous Helmholtz eq. Distribution of source components November 29, 2016 [Koyama+ ICASSP 2014] Sound field is divided into two regions
  10. 10. Generative model of sound field  Inhomogeneous and homogeneous Helmholtz eq. November 29, 2016 [Koyama+ ICASSP 2014] Green’s function Inhomogeneous + homogeneous terms Plane wave
  11. 11. November 29, 2016 Generative model of sound field  Observe sound pressure distribution on plane  Conversion into driving signals Synthesize monopole sources [Spors+ AES Conv. 2008] Ambient componentsDirect source components Applying WFR filtering method [Koyama+ IEEE TASLP 2013] Decomposition into two components can lead to higher reproduction accuracy above spatial Nyquist freq
  12. 12. November 29, 2016 Sparse sound field representation ・・・・・・・・ Microphone array Source components Grid points Sparsity-based signal decomposition Discretization Ambient components Dictionary matrix of Green’s functions Observed signal Distribution of source components A few elements of has non-zero values under the assumption of spatially sparse source distribution
  13. 13. Sparse signal decomposition  Sparse signal representation in vector form  Signal decomposition based on sparsity of November 29, 2016 Minimize -norm of
  14. 14. Group sparsity based on physical properties November 29, 2016 Group sparse signal models for robust decomposition • Multiple time frames • Temporal frequencies • Multipole components Decomposition algorithm extending FOCUSS [Koyama+ ICASSP 2015]  Sparse signal representation in vector form Structure of sparsity induced by physical properties
  15. 15. Block diagram of signal conversion  Decomposition stage – Group sparse decomposition of  Reconstruction stage – and are respectively converted into driving signals – is obtained as sum of two components November 29, 2016
  16. 16. Simulation Experiment  Proposed method (Proposed), WFR filtering method (WFR), and Sound Pressure Control method (SPC) were compared  32 microphones (6 cm intervals)and 48 loudspeakers (4 cm intervals)  : Rectangular region of 2.4x2.4 m, Grid points: (10cm, 20cm) intervals  Source directivity: unidirectional  Source signal: single frequency sinewave Recording area Target area November 29, 2016
  17. 17. Simulation Experiment  Signal-to-distortion ratio of reproduction (SDRR) Recording area Target area November 29, 2016 Original pressure distribution Reproduced pressure distribution
  18. 18. November 29, 2016 Frequency vs. SDR SDRRs above spatial Nyquist frequency were improved  Source location: (-0.32, -0.84, 0.0) m
  19. 19. Reproduced sound pressure distribution (1.0 kHz)PressureError November 29, 2016 Proposed WFR SPC 18.1 dB 18.0 dB 19.4 dB  Source location: (-0.32, -0.84, 0.0) m SDRR:
  20. 20. Reproduced sound pressure distribution (4.0 kHz)PressureError November 29, 2016 19.7 dB 6.8 dB 7.8 dB Proposed WFR SPC  Source location: (-0.32, -0.84, 0.0) m SDRR:
  21. 21. Frequency response of reproduced sound field November 29, 2016  Frequency response at (0.0, 1.0, 0.0) m Reproduced frequency response was improved
  22. 22. Conclusion  Super-resolution sound field recording and reproduction based on sparse representation – Conventional plane wave decomposition is suffered from spatial aliasing artifacts – Sound field representation using source and plane wave components – Sound field decomposition based on spatial sparsity of source components – Group sparsity based on physical properties of sound field – Experimental results indicated that reproduction accuracy above spatial Nyquist frequency can be improved November 29, 2016 Thank you for your attention!
  23. 23. Related publications • S. Koyama and H. Saruwatari, “Sound field decomposition in reverberant environment using sparse and low-rank signal models,” Proc. IEEE ICASSP, 2016. • N. Murata, S. Koyama, et al. “Sparse sound field decomposition with multichannel extension of complex NMF,” Proc. IEEE ICASSP, 2016. • S. Koyama, et al. “Sparse sound field decomposition using group sparse Bayesian learning,” Proc. APSIPA ASC, 2015. • N. Murata, S. Koyama, et al. “Sparse sound field decomposition with parametric dictionary learning for super-resolution recording and reproduction,” Proc. IEEE CAMSAP, 2015. • S. Koyama, et al. “Source-location-informed sound field recording and reproduction with spherical arrays,” Proc. IEEE WASPAA, 2015. • S. Koyama, et al. “Source-location-informed sound field recording and reproduction,” IEEE J. Sel. Topics Signal Process., vol. 9, no. 5, pp. 881-894, 2015. • S. Koyama, et al. “Structured sparse signal models and decomposition algorithm for super-resolution in sound field recording and reproduction,” Proc. IEEE ICASSP, 2015. • S. Koyama, et al. “Sparse sound field representation in recording and reproduction for reducing spatial aliasing artifacts,” Proc. IEEE ICASSP, 2014. November 29, 2016

×