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

7 Multi-Channel Sound Acquisition Using a Multi-Wave Sound Field Model

Oliver Thiergart and Emanuël Habets

International Audio Laboratories Erlangen, a Joint Institution of the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Fraunhofer IIS, Erlangen, Germany

7.1 Introduction

During the last decade, different devices with two or more microphones have emerged that enable multi-channel sound acquisition. Typical examples include mobile phones, digital cameras, and smart television screens, which can be used for a huge variety of audio applications including hands-free communication, spatial sound recording, and speech-based human–machine interaction. Key to the realization of these applications with different devices is flexible and efficient sound acquisition and processing. “Flexible” means that the sound can be captured with different microphone configurations while being able to generate one or more desired output signals at the reproduction side depending on the application. “Efficient” means that only a few audio signals, in comparison to the number of microphones used, need to be transmitted to the reproduction side while maintaining full flexibility.

This flexible and efficient sound acquisition and processing can be achieved using a parametric description of the spatial sound. This approach is used, for example, in directional audio coding (DirAC) as discussed in Chapter 5 (Pulkki, 2007) or high angular resolution plane-wave expansion (HARPEX; Berge and Barrett, ...