book

Parametric Time-Frequency Domain Spatial Audio

by Ville Pulkki, Symeon Delikaris-Manias, Archontis Politis

December 2017

Intermediate to advanced

409 pages

14h 8m

English

Wiley-IEEE Press

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Notes
1.1 Introduction1.2 Time–Frequency Processing1.3 Processing of Spatial AudioNoteReferences
2.1 Introduction2.2 Sound Field Measurement by a Spherical Array2.3 Array Processing and Plane-Wave Decomposition2.4 Sensitivity to Noise and Standard Regularization Methods2.5 Optimal Noise-Robust Design2.6 Spatial Aliasing and High Frequency Performance Limit2.7 High Frequency Bandwidth Extension by Aliasing Cancellation2.8 High Performance Broadband PWD Example2.9 Summary2.10 AcknowledgmentReferences
3.1 Introduction3.2 The Plane-Wave Decomposition Problem3.3 Bayesian Approach to Plane-Wave Decomposition3.4 Calculating the IRLS Noise-Power Regularization Parameter3.5 Numerical Simulations3.6 Experiment: Echoic Sound Scene Analysis3.7 ConclusionsAppendixReferences
4.1 Introduction4.2 Parametric Processing OverviewReferences
5.1 Representing Spatial Sound with First-Order B-Format Signals5.2 Some Notes on the Evolution of the Technique5.3 DirAC with Ideal B-Format Signals5.4 Analysis of Directional Parameters with Real Microphone Setups5.5 First-Order DirAC with Monophonic Audio Transmission5.6 First-Order DirAC with Multichannel Audio Transmission5.7 DirAC Synthesis for Headphones and for Hearing Aids5.8 Optimizing the Time–Frequency Resolution of DirAC for Critical Signals5.9 Example Implementation5.10 SummaryReferences

6.1 Introduction6.2 Sound Field Model6.3 Energetic Analysis and Estimation of Parameters6.4 Synthesis of Target Setup Signals6.5 Subjective Evaluation6.6 ConclusionsNoteReferences
7.1 Introduction7.2 Parametric Sound Acquisition and Processing7.3 Multi-Wave Sound Field and Signal Model7.4 Direct and Diffuse Signal Estimation7.5 Parameter Estimation7.6 Application to Spatial Sound Reproduction7.7 SummaryNotesReferences
8.1 Introduction8.2 Notation and Signal Model8.3 Overview of the Method8.4 Loudspeaker-Based Spatial Sound Reproduction8.5 Binaural-Based Spatial Sound Reproduction8.6 ConclusionsReferences
9.1 Introduction9.2 Spectrogram Factorization9.3 Array Signal Processing and Spectrogram Factorization9.4 Applications of Spectrogram Factorization in Spatial Audio9.5 Discussion9.6 Matlab ExampleNoteReferences
10.1 Introduction10.2 Signal-Independent Enhancement10.3 Signal-Dependent EnhancementReferences
11.1 Introduction11.2 Notation and Signal Model11.3 Estimation of the Cross-Spectrum-Based Post-Filter11.4 Implementation Examples11.5 Conclusions and Further Remarks11.6 Source CodeNoteReferences
12.1 Introduction12.2 Time–Frequency Masks for Speech Enhancement Using Supervised Learning12.3 Artificial Neural Networks12.4 Mask Learning: A Simulated Example12.5 Mask Learning: A Real-World Example12.6 Conclusions12.7 Source CodeNotesReferences
13.1 Introduction13.2 Stereo-to-Multichannel Upmix Processor13.3 Digitally Enhanced Shotgun Microphone13.4 Surround Microphone System Based on Two Microphone Elements13.5 SummaryReferences
14.1 Introduction14.2 Parametric Sound Scene Synthesis for Virtual Reality14.3 Spatial Manipulation of Sound Scenes14.4 SummaryReferences
15.1 Introduction and Motivation15.2 Background15.3 Immersive Audio Communication System (ImmACS)15.4 Capture and Reproduction of Crowded Acoustic Environments15.5 ConclusionsNotesReferences

Content preview from Parametric Time-Frequency Domain Spatial Audio

15 Parametric Spatial Audio Techniques in Teleconferencing and Remote Presence

Anastasios Alexandridis, Despoina Pavlidi, Nikolaos Stefanakis, and Athanasios Mouchtaris

Foundation for Research and Technology-Hellas, Institute of Computer Science (FORTH-ICS), Heraklion, Crete, Greece

15.1 Introduction and Motivation

In this chapter, applications of time–frequency parametric spatial audio techniques are presented in the areas of teleconferencing and remote presence. Specifically, the focus is on using circular microphone arrays in order to capture the sound field in these two different application areas. The methods presented here, consequently, have the scope to process multi-microphone signals so that the spatial properties of the original sound field can be reproduced using a multichannel loudspeaker setup or headphones as faithfully as possible, keeping as a priority computational efficiency, so that the proposed approaches can be implemented in real time using a typical PC processor.

The approaches presented are not simply extensions of established work into particular application areas, but are based on algorithms that are novel and innovative. It is relevant at this point to explain the main difference between the two application areas examined in this chapter. On the one hand, teleconference applications typically involve a small number of simultaneous sound sources at each remote participant site (the speakers at a given time instant), and the objective is to estimate ...