Comparison of energy-preserving and all-round Ambisonic decoders

Slides:



Advertisements
Similar presentations
The Complexity of Linear Dependence Problems in Vector Spaces David Woodruff IBM Almaden Joint work with Arnab Bhattacharyya, Piotr Indyk, and Ning Xie.
Advertisements

Spatial point patterns and Geostatistics an introduction
D1 - 29/05/2014 France Télécom Recherche & Développement Workshop « From 5.1 to Sound Field Synthesis..." AES 120th Convention, Paris 2006 Higher Order.
Spatial Sound Encoding Including Near Field Effect: Introducing Distance Coding Filters and a Viable, New Ambisonic Format Jérôme Daniel, France Telecom.
Isoparametric Elements Element Stiffness Matrices
Synthesizing naturally produced tokens Melissa Baese-Berk SoundLab 12 April 2009.
Lecture 15 Orthogonal Functions Fourier Series. LGA mean daily temperature time series is there a global warming signal?
Physics 114: Lecture 7 Uncertainties in Measurement Dale E. Gary NJIT Physics Department.
Chapter 23 Gauss’ Law.
Train DEPOT PROBLEM USING PERMUTATION GRAPHS
1cs542g-term Notes  Make-up lecture tomorrow 1-2, room 204.
Reading population codes: a neural implementation of ideal observers Sophie Deneve, Peter Latham, and Alexandre Pouget.
Isoparametric Elements Element Stiffness Matrices
Chapter 3 Data and Signals
Location Estimation in Sensor Networks Moshe Mishali.
Digital Voice Communication Link EE 413 – TEAM 2 April 21 st, 2005.
Variability Measures of spread of scores range: highest - lowest standard deviation: average difference from mean variance: average squared difference.
Project Presentation: March 9, 2006
EE2F2 - Music Technology 11. Physical Modelling Introduction Some ‘expressive instruments don’t sound very convincing when sampled Examples: wind or.
Chapter 23 Gauss’s Law.
2. Solving Schrödinger’s Equation Superposition Given a few solutions of Schrödinger’s equation, we can make more of them Let  1 and  2 be two solutions.
4. The Postulates of Quantum Mechanics 4A. Revisiting Representations
Binaural Sound Localization and Filtering By: Dan Hauer Advisor: Dr. Brian D. Huggins 6 December 2005.
CS Subdivision I: The Univariate Setting Peter Schröder.
Applications in GIS (Kriging Interpolation)
Noise, Information Theory, and Entropy
1/19 Philip Coleman, Philip J. B. Jackson, Marek Olik Centre for Vision, Speech and Signal Processing, University.
Doppler Radar From Josh Wurman NCAR S-POL DOPPLER RADAR.
3.1 - Solving Systems by Graphing. All I do is Solve!
Daniel Kroening and Ofer Strichman Decision Procedures An Algorithmic Point of View Deciding ILPs with Branch & Bound ILP References: ‘Integer Programming’
Computer Vision Spring ,-685 Instructor: S. Narasimhan Wean 5403 T-R 3:00pm – 4:20pm.
ROOM ACOUSTIC MEASUREMENTS ACOUSTICS OF CONCERT HALLS AND ROOMS Handbook of Acoustics, Chapter 9 Schroeder (1965)
Digital to Analog Converters (DAC) 1
Lecture 1 Signals in the Time and Frequency Domains
Decision Procedures An Algorithmic Point of View
THEORETICAL STUDY OF SOUND FIELD RECONSTRUCTION F.M. Fazi P.A. Nelson.
Transformations Aaron Bloomfield CS 445: Introduction to Graphics
GG 313 Lecture 26 11/29/05 Sampling Theorem Transfer Functions.
Oscillations & Waves IB Physics. Simple Harmonic Motion Oscillation 4. Physics. a. an effect expressible as a quantity that repeatedly and regularly.
Shape Matching for Model Alignment 3D Scan Matching and Registration, Part I ICCV 2005 Short Course Michael Kazhdan Johns Hopkins University.
V. Space Curves Types of curves Explicit Implicit Parametric.
Time Series Spectral Representation Z(t) = {Z 1, Z 2, Z 3, … Z n } Any mathematical function has a representation in terms of sin and cos functions.
Wireless and Mobile Computing Transmission Fundamentals Lecture 2.
The Care and Feeding of Loudness Models J. D. (jj) Johnston Chief Scientist Neural Audio Kirkland, Washington, USA.
Matrices Addition & Subtraction Scalar Multiplication & Multiplication Determinants Inverses Solving Systems – 2x2 & 3x3 Cramer’s Rule.
Progress in identification of damping: Energy-based method with incomplete and noisy data Marco Prandina University of Liverpool.
Modal Analysis of Rigid Microphone Arrays using Boundary Elements Fabio Kaiser.
Set Analysis. Agenda 1. What is Set Analysis? 2. Why do we use it? 3. How do we use it (syntax)? 4. Examples.
1“Principles & Applications of SAR” Instructor: Franz Meyer © 2009, University of Alaska ALL RIGHTS RESERVED Dr. Franz J Meyer Earth & Planetary Remote.
An Alternative Ambisonics Formulation: Modal Source Strength Matching and the Effect of Spatial Aliasing Franz Zotter Hannes Pomberger Matthias Frank.
WEATHER SIGNALS Chapter 4 (Focus is on weather signals or echoes from radar resolution volumes filled with countless discrete scatterers---rain, insects,
Chapter 13 Gravitation Newton’s Law of Gravitation Here m 1 and m 2 are the masses of the particles, r is the distance between them, and G is the.
1.  In the words of Bowley “Dispersion is the measure of the variation of the items” According to Conar “Dispersion is a measure of the extent to which.
Chapter 8 Engineering Geometry
A Brief Journey into Parallel Transmit Jason Su. Description Goal: expose myself to some of the basic techniques of pTx –Replicate in-class results –Explore.
Computer Graphics & Image Processing Chapter # 4 Image Enhancement in Frequency Domain 2/26/20161.
Lecture 8: Measurement Errors 1. Objectives List some sources of measurement errors. Classify measurement errors into systematic and random errors. Study.
Introduction to Data Conversion EE174 – SJSU Tan Nguyen.
CSE 554 Lecture 8: Alignment
Time Series Spectral Representation
Chapter 13 Gravitation.
Analyzing Redistribution Matrix with Wavelet
CHE 391 T. F. Edgar Spring 2012.
Roberto Battiti, Mauro Brunato
2. Solving Schrödinger’s Equation
Chapter 13 Gravitation.
Signals.
CHAPTER – 1.2 UNCERTAINTIES IN MEASUREMENTS.
CHAPTER – 1.2 UNCERTAINTIES IN MEASUREMENTS.
Presentation transcript:

Comparison of energy-preserving and all-round Ambisonic decoders Franz Zotter Matthias Frank Hannes Pomberger

Vector Base Amplitude Panning selects a loudspeaker pair (base) to vector pan with all-positive gains (pairs ≤90°)

… for irregular layouts it still does the job easy (throw-away loudspeaker retains some outside signal)

Performance measures: width slightly fluctuates Level and width estimators for VBAP on irregular layout

Ambisonic panning is a little bit different: it assumes a virtual panning function (here horizontal-only) infinite order enc red>0, blue<0: infinite resolution. infty -infty

Ambisonic panning is a little bit different: it assumes a virtual panning function (here horizontal-only) infinite order red>0, blue<0: infinite resolution. infty -infty

Ambisonic panning is a little bit different: it assumes a virtual panning function (here horizontal-only) finite order red>0, blue<0: infinite resolution. infty -infty Now we should be able to sample: circular/spherical polynomial discretization rules exist.

Optimally Sampled Ambisonics with max-rE Always easy if we have optimal layout…

What is an optimal layout? 2D examples: regular polygon setups, N=3, L=6 N=3, L=7 N=3, L=8

What is an optimal layout? 2D examples: regular polygon setups, N=3, L=6 N=3, L=7 N=3, L=8

What is an optimal layout? 2D examples: regular polygon setups, N=3, L=6 N=3, L=7 N=3, L=8 Perfect width, loudness, direction measures: Circular/Spherical t-designs with t ≥ 2N+1 Circular t-designs: regular polygons of t+1 nodes: easy

Spherical t-designs allow to express integrals as sums without additional weighting or matrix inversions: integral-mean over any order t spherical polynomial is equivalent to summation across nodes of the t-design. Applicable to measures of E if t ≥ 2N, and of rE if t ≥ 2N+1 given the order N t-designs: t = 3 (octahedron, N=1), 5 (icosahedron, N=2), 7 (N=3), 9 (N=4).

What about non-uniform arrangements?

Performance measures for the simplest decoder: sampling With max rE weights

Performance measures for the simplest decoder: sampling With max rE weights (left) in comparison to VBAP (right)

More elaborate: Mode matching decoder (??)

Performance measures for mode-matching decoder: unstable With max rE weights Nicer, but gains reach a lot of dB outside panning range…

Is Ambisonic Decoding too complicated?

What we consider a break through… Energy preserving Ambisonic Decoding: [Franz Zotter, Hannes Pomberger, Markus Noisternig: „Energy-Preserving Ambisonic Decoding“, Journal: acta acustica, Jan. 2011.] [Hannes Pomberger, Franz Zotter: „Ambisonic Panning with constant energy constraint“, Conf: DAGA, 2012.] All-Round Ambisonic Decoding: [Franz Zotter, Matthias Frank, Alois Sontacchi: „Virtual t-design Ambisonics Rig Using VBAP“, Conf: EAA Euroregio, Ljubljana, 2010] [Franz Zotter, Matthias Frank, „All-Round Ambisonic Panning and Decoding“: Journal: AES, Oct. 2012]

1st Step: Slepian functions for target angles (semi-circle) These would be all:

1st Step: Slepian functions for target angles (semi-circle) Reduced to smaller number (those dominant on lower semicircle discarded) Loudspeakers are then encoded in a the reduced set of functions

2nd Step: energy-preserving decoding: Instead of Use closest row-orthogonal matrix for decoding: Ambisonic Sound Field Recording and Reproduction

Virtual decoding to large optimal layout Decoder is the transpose (optimal virtual layout) Playback of optimal layout to real loudspeakers: VBAP Ambisonic order can now be freely selected! N -> infty yields VBAP. Number of virtual loudspeakers should be large Ambisonic Sound Field Recording and Reproduction

Energy-preserving decoder vs. AllRAD Ambisonic Sound Field Recording and Reproduction

Performance measures energy-preseving vs AllRAD With max rE weights Energy-preserving: perfect amplitude, All-RAD: better localization measures, easier calculation

Concluding: flexible versus robust AllRAD is very flexible and always easy to calculate but not as smooth in loudness. Order is variable, but an optimally smooth one exists. Energy-preserving is mathematically more challengeing but useful for high-quality decoding (in terms of amplitude). Important for audio material that is recorded or produced in Ambisonics. Ambisonic Sound Field Recording and Reproduction

Thanks! Advancements of Ambisonics

VBAP and Ambisonics compared Triplet-wise panning (VBAP) + constant loudness + arbitrary layout -- varying spread Ambisonic Panning ~+ constant loudness + arbitrary layout ~+ invariant spread

Virtual t-design Ambisonics using VBAP: modified Fig. 7: Energy measure [dB], and spread measure [°] as a function of the virtual source direction. [Frank, Zotter 201*] 9/13

Virtual t-design Ambisonics using VBAP: modified Fig. 7: Energy measure [dB], and spread measure [°] as a function of the virtual source direction. [Frank, Zotter 201*] 9/13

Virtual t-design Ambisonics using VBAP: modified Fig. 7: Energy measure [dB], and spread measure [°] as a function of the virtual source direction. [Frank, Zotter 201*] 9/13

Virtual t-design Ambisonics using VBAP: modified Fig. 7: Energy measure [dB], and spread measure [°] as a function of the virtual source direction. [Frank, Zotter 201*] 9/13

Virtual t-design Ambisonics using VBAP: modified Fig. 7: Energy measure [dB], and spread measure [°] as a function of the virtual source direction. [Frank, Zotter 201*] 9/13

Energy-preserving decoder All-round Ambisonic decoder