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Table of contents
Contents
List of Figures
List of Tables
General Introduction
0.1 What is loudness?
0.1.1 Definition
0.1.2 Spectral aspects of loudness
0.1.3 Temporal aspects of loudness
0.1.4 Loudness of natural sounds in natural environments .
0.1.5 The dimension of loudness investigated in this thesis
0.2 Measuring loudness of time-varying sounds
0.2.1 Generalities
0.2.2 Methodological considerations
0.2.3 Issues related to global loudness measurement
0.2.4 Methods considered in this thesis
0.3 Predicting (time-varying) loudness with current models .
0.3.1 The simplest loudness model: Stevens’ power law .
0.3.2 Loudness models for time-varying sounds
0.3.2.1 Early stages taken from stationary loudness models
0.3.2.2 Late temporal integration stages
0.3.2.3 Computing global loudness
0.4 Context of this thesis
0.4.1 Current issues related to the loudness of time-varying sounds
0.4.1.1 Global loudness of long time-varying sounds xliv
0.4.1.2 Sounds with rising vs. falling amplitude envelopes: Perceptual “asymmetries” at different timescales
0.4.1.4 A novel perspective: Temporal weighting of loudness
0.4.1.5 Investigating intensity coding as a complementary approach
0.4.2 Approach of the present thesis
0.4.3 The LoudNat project
0.4.4 Organization of the manuscript
1 A robust asymmetry in loudness between rising and falling intensity tones
1.1 Introduction
1.2 Experiment 1
1.2.1 Method
1.2.1.1 Participants
1.2.1.2 Apparatus
1.2.1.3 Stimuli
1.2.1.4 Procedure
1.2.1.5 Normalization and data analysis
1.2.2 Results
1.2.2.1 Loudness asymmetry
1.2.2.2 Global context effect
1.2.2.3 Local context effect
1.2.3 Discussion
1.3 Experiment 2
1.3.1 Method
1.3.1.1 Participants
1.3.1.2 Apparatus
1.3.1.3 Stimuli
1.3.1.4 Procedure
1.3.2 Results
1.3.2.1 Time order errors
1.3.2.2 Loudness asymmetries
1.3.3 Discussion
1.4 General Discussion
1.4.1 Predictions from current loudness models
1.4.2 Conclusions and perspectives
Partial synthesis
2 Influences of spectral structure and intensity-region on the loudness asymmetry
2.1 Introduction
2.2 Experiment 1
2.2.1 Materials and method
2.2.1.1 Participants
2.2.1.2 Stimuli
2.2.1.3 Apparatus
2.2.1.4 Procedure
2.2.2 Results
2.2.3 Discussion
2.3 Experiment 2
2.3.1 Materials and method
2.3.1.1 Participants
2.3.1.2 Stimuli
2.3.1.3 Apparatus
2.3.1.4 Preliminary experiment: Individual tone/noise loudness equalization
2.3.1.5 Procedure
2.3.2 Results
2.3.3 Discussion
2.4 General discussion and conclusion
3 How temporal profile characteristics of rising and falling tones shape their global loudness
3.1 Introduction
3.2 Experiment 1
3.2.1 Materials and method
3.2.1.1 Participants
3.2.1.2 Stimuli
3.2.1.3 Apparatus
3.2.1.4 Procedure
3.2.2 Results
3.2.3 Discussion
3.3 Experiment 2
3.3.1 Materials and method
3.3.1.1 Participants
3.3.1.2 Stimuli
3.3.1.3 Apparatus
3.3.1.4 Procedure
3.3.2 Results
3.3.2.1 Analysis A – Loudness of constant tones with durations between 2 and 12 s
3.3.2.2 Analysis B – Global loudness of rising and falling ramps varying at 2.5 dB/s
3.3.2.3 Analysis C – Global loudness of rising and falling ramps varying at 5 dB/s
3.3.2.4 Analysis D – Global loudness of rising ramps varying at 2.5 dB/s and 5 dB/s
3.3.2.5 Analysis E – Global loudness of falling ramps varying at 2.5 dB/s and 5 dB/s
3.3.3 Discussion
3.4 General discussion and conclusion
3.4.1 Summary of the present findings
3.4.2 Predicting global loudness from Glasberg and Moore’s model outputs
3.4.3 Conclusion and perspectives
4 Temporal loudness weights of rising and falling tones
4.1 Introduction
4.2 Materials and method
4.2.1 Subjects
4.2.2 Stimuli
4.2.3 Apparatus
4.2.4 Procedure
4.2.5 Data analysis
4.3 Results
4.3.1 Temporal weights for the flat profile
4.3.2 Temporal weights for the increasing and decreasing profiles
4.3.3 Loudness difference between up-ramps and down-ramps
4.4 Discussion and conclusion
Partial synthesis
General Discussion
5.1 Characterization of the global loudness asymmetry between rising and falling sounds
5.1.1 Apparent robustness and constancy of the phenomenon
5.1.2 Influences of the physical attributes of the sounds .
5.1.2.1 Intensity-region
5.1.2.2 Spectral structure
5.1.2.3 Temporal profile characteristics
5.2 Potential mechanisms underlying the asymmetry
5.2.1 Temporal weighting of loudness
5.2.2 Reduction at high intensity-regions and local context emphasis
5.2.3 Decay of the loudness trace of falling stimuli
5.2.4 A specific circuit for rising tones?
5.2.5 Non-sensory factors
5.3 Global loudness processing of time-varying sounds
5.4 Loudness models for time-varying sounds
5.4.1 Predictions of global loudness asymmetries
5.4.2 Challenges for future loudness models
5.5 Main research perspectives derived from this thesis
5.5.1 Identifying the processes highlighted in this thesis
5.5.2 Assessing the role of memory for falling tones
5.5.3 Disentangling decision criterion effects
5.5.4 Achieving a better understanding of temporal integration processes
5.5.5 Investigating global loudness processing with complex natural stimuli
5.5.6 Determining the neural bases of auditory asymmetries
A Interindividual variability in the size of the asymmetry
B Asymmetries with narrow-band noises
C Correlation analyses of the asymmetries measured in Chapter
D Size of the asymmetries in Chapter 3
E Psychometric functions derived from the complementary experiment in Chapter 4
F Modeling global loudness processing of time-varying sounds: A second-order reverse correlation analysis
F.1 Introduction
F.2 Inferring observers’ behavior from both linear and nonlinear kernels
F.2.1 First-order kernels
F.2.2 Second-order kernels
F.2.3 Learning observed in second-order kernels
F.2.4 System identification through second-order kernel properties
F.3 Modeling
F.3.1 Potential modeling structure
F.3.2 Computations
F.3.3 Limitations of the model
F.4 General discussion
G Temporal weighting of loudness in two different loudnessjudgment tasks
G.1 Introduction
G.2 Experiment 1
G.2.1 Materials and method
G.2.1.1 Participants
G.2.1.2 Stimuli
G.2.1.3 Apparatus
G.2.1.4 Procedure
G.2.1.5 Fitting loudness functions
G.2.1.6 Decisions models
G.2.2 Results
G.2.2.1 Loudness functions
G.2.2.2 Temporal weighting patterns
G.2.2.3 Predictive power of the decision models .
G.2.2.4 Increased predictive power by including temporal weights
G.2.2.5 Additional analyses per mean level in the AME task
G.2.3 Discussion
G.3 Experiment 2
G.3.1 Materials and method
G.3.1.1 Participants
G.3.1.2 Stimuli and apparatus
G.3.1.3 Procedure
G.3.1.4 Fitting loudness functions
G.3.1.5 Decisions models
G.3.2 Results
G.3.2.1 Loudness functions
G.3.2.2 Temporal weighting patterns
G.3.2.3 Predictive power of the decision models .
G.3.2.4 Increased predictive power by including temporal weights
G.3.3 Discussion
G.4 General discussion and conclusion
Bibliography
