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

1 Time–Frequency Processing: Methods and Tools

Juha Vilkamo¹ and Tom Bäckström²

¹Nokia Technologies, Finland

²Department of Signal Processing and Acoustics, Aalto University, Finland

1.1 Introduction

In most audio applications, the purpose is to reproduce sounds for human listening, whereby it is essential to design and optimize systems for perceptual quality. To achieve such optimal quality with given resources, we often use principles in the processing of signals that are motivated by the processes involved in hearing. In the big picture, human hearing processes the sound entering the ears in frequency bands (Moore, 1995). The hearing is thus sensitive to the spectral content of ear canal signals, which changes quickly with time in a complex way. As a result of frequency-band processing, the ear is not particularly sensitive to small differences in weaker sounds in the presence of a stronger masking sound near in frequency and time to the weaker sound (Fastl and Zwicker, 2007). Therefore, a representation of audio signals where we have access to both time and frequency information is a well-motivated choice.

A prerequisite for efficient audio processing methods is a representation of the signal that presents features desirable to hearing in an accessible form and also allows high-quality playback of signals. Useful properties of such a representation are, for example, that its coefficients have physically or perceptually relevant interpretations, and that the coefficients ...