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Evaluation of techniques for navigation of higher- order ambisonics - - PowerPoint PPT Presentation

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Evaluation of techniques for navigation of higher- order ambisonics Acoustics 17 Boston Presentation 1pPPb4 June 25th, 2017 Joseph G. Tylka (presenter) and Edgar Y. Choueiri 3D Audio and Applied Acoustics (3D3A) Laboratory Princeton

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Evaluation of techniques for navigation of higher-

  • rder ambisonics

Acoustics ’17 Boston Presentation 1pPPb4 June 25th, 2017 Joseph G. Tylka (presenter) and Edgar Y. Choueiri 3D Audio and Applied Acoustics (3D3A) Laboratory Princeton University www.princeton.edu/3D3A

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SLIDE 2

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HOA microphone Sound source

Sound Field Navigation

HOA mic. 2 HOA mic. 3 HOA mic. 4

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Sound Field Navigation

  • Lots of different ways to navigate:
  • Plane-wave translation (Schultz & Spors, 2013)
  • Spherical-harmonic re-expansion (Gumerov & Duraiswami, 2005)
  • Linear interpolation/“crossfading” (Southern et al., 2009)
  • Collaborative blind source separation (Zheng, 2013)
  • Regularized least-squares interpolation (Tylka & Choueiri, 2016)
  • Need a way to evaluate and compare them
  • Isolate navigational technique from binaural/ambisonic rendering
  • Subjective testing can be lengthy/costly ⟹ Objective Metrics

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HOA in ↓ HOA out

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Overview

  • For each quality (localization and coloration):
  • Existing metrics
  • Proposed metric
  • Listening test
  • Results
  • Summary and outlook

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Source Localization

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Existing Metrics

  • Binaural models:
  • Lindemann (1986); Dietz et al. (2011); etc.
  • Predict perceived source azimuth given binaural impulse responses (IRs)
  • Localization vectors:
  • Gerzon (1992) — for analyzing ambisonics
  • Low frequency (velocity) and high frequency (energy) vectors
  • Predict perceived source direction given speaker positions & gains
  • Stitt et al. (2016)
  • Incorporates precedence effect to Gerzon’s energy vector
  • Model requires: direction-of-arrival, time-of-arrival, and amplitude for each source
  • Tylka & Choueiri (2016) generalized algorithm for ambisonics IRs

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Proposed Metric

1.Transform to plane-wave impulse responses (IRs) 2.Split each IR into wavelets 3.Threshold to find onset times 4.Compute average amplitude in each critical band 5.Compute Stitt’s energy vector in each band for f ≥ 700 Hz 6.Similarly, compute velocity vector in each band for f ≤ 700 Hz 7.Compute average vector weighted by stimulus energies in each band

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Plane-wave IR High-pass Find peaks Wavelets Window

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SLIDE 8

Localization Test

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10 cm 127 cm θ 5 cm

15 14 13 12 11 10 … … Recording/encoding Interpolation

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SLIDE 9

Localization Test Results

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  • 30
  • 20
  • 10

10 20 30

Predicted azimuth (°)

  • 30
  • 20
  • 10

10 20 30

Measured azimuth (°) All Results

Pearson correlation coefficient: r = 0.77 Mean absolute error: ε = 3.67° Test details:

  • 70 test samples
  • 4 trained listeners
  • Speech signal
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SLIDE 10

Spectral Coloration

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Existing Metrics

  • Auditory band error (Schärer & Lindau, 2009);

peak and notch errors (Boren et al., 2015)

  • Central spectrum (Kates, 1984; 1985)
  • Composite loudness level (Pulkki et al., 1999;

Huopaniemi et al., 1999)

  • Internal spectrum and A0 measure (Salomons,

1995; Wittek et al., 2007)

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Free-field transfer functions

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Binaural transfer functions

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Methodology

  • Perform multiple linear regression between ratings and various metrics
  • For spectral metrics: compute max−min & standard deviation
  • MUltiple Stimuli with Hidden Reference and Anchor (ITU-R BS.1534-3)
  • Reference: no navigation, pink noise
  • Anchor 1: 3.5 kHz low-passed version of Ref.
  • Anchor 2: +6 dB high-shelf above 7 kHz applied to Ref.
  • Test samples: vary interpolation technique and distance
  • User rates each sample from 0–100: 100 = Ref.; 0 = Anchor 1
  • Coloration score = 100 − MUSHRA rating: 0 = Ref.; 100 = Anchor 1
  • Proposed model: auditory band and notch errors only (Boren et al., 2015)

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  • 20

20 40 60 80 100 120

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20 40 60 80 100 120

  • Avg. Measured Coloration Score

Proposed: r = 0.84

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20 40 60 80 100 120

  • 20

20 40 60 80 100 120

Kates: r = 0.72

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20 40 60 80 100 120

Predicted Coloration Score

  • 20

20 40 60 80 100 120

  • Avg. Measured Coloration Score

Pulkki et al.: r = 0.79

  • 20

20 40 60 80 100 120

Predicted Coloration Score

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20 40 60 80 100 120

Wittek et al.: r = 0.77

Regression Results

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Legend Data/model y = x y = x ± 20 − − —

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SLIDE 14

Summary and Outlook

  • Presented objective metrics that predict localization and

coloration

  • Validated through comparisons with subjective test results

Next Steps:

  • 1. Compare localization metric with binaural models
  • 2. Validate metrics for other stimuli, directions, conditions
  • 3. Verify generalization to other binaural rendering

techniques

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SLIDE 15

References

  • Boren et al. (2015). “Coloration metrics for headphone equalization.”
  • Dietz et al. (2011). “Auditory model based direction estimation of concurrent speakers from binaural signals.”
  • Gerzon (1992). “General Metatheory of Auditory Localisation.”
  • Gumerov and Duraiswami (2005). Fast Multipole Methods for the Helmholtz Equation in Three Dimensions.
  • Huopaniemi et al. (1999). “Objective and Subjective Evaluation of Head-Related Transfer Function Filter Design.”
  • ITU-R BS.1534-3 (2015). “Method for the subjective assessment of intermediate quality level of audio systems.”
  • Kates (1984). “A Perceptual Criterion for Loudspeaker Evaluation.”
  • Kates (1985). “A central spectrum model for the perception of coloration in filtered Gaussian noise.”
  • Lindemann (1986). “Extension of a binaural cross-correlation model by contralateral inhibition.”
  • Pulkki et al. (1999). “Analyzing Virtual Sound Source Attributes Using a Binaural Auditory Model.”
  • Salomons (1995). Coloration and Binaural Decoloration of Sound due to Reflections.
  • Schärer and Lindau (2009). “Evaluation of Equalization Methods for Binaural Signals.”
  • Schultz and Spors (2013). “Data-Based Binaural Synthesis Including Rotational and Translatory Head-Movements.”
  • Southern, Wells, and Murphy (2009). “Rendering walk-through auralisations using wave-based acoustical models.”
  • Stitt, Bertet, and van Walstijn (2016). “Extended Energy Vector Prediction of Ambisonically Reproduced Image Direction at Off-

Center Listening Positions.”

  • Tylka and Choueiri (2016). “Soundfield Navigation using an Array of Higher-Order Ambisonics Microphones.”
  • Wittek et al. (2007). “On the sound colour properties of wavefield synthesis and stereo.”
  • Zheng (2013). Soundfield navigation: Separation, compression and transmission.

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Acknowledgments

  • Binaural rendering was performed using M. Kronlachner’s ambiX plug-ins: http://www.matthiaskronlachner.com/?p=2015
  • The em32 Eigenmike by mh acoustics was used to measure the HOA RIRs: https://mhacoustics.com/products#eigenmike1
  • Auditory filters were generated using the LTFAT MATLAB Toolbox: http://ltfat.sourceforge.net/
  • P. Stitt’s energy vector code can be found here: https://circlesounds.wordpress.com/matlab-code/