- •Preface
- •Acknowledgments
- •Contents
- •1 Introduction
- •1.1 Auditory Temporal and Spatial Factors
- •1.2 Auditory System Model for Temporal and Spatial Information Processing
- •2.1 Analysis of Source Signals
- •2.1.1 Power Spectrum
- •2.1.2 Autocorrelation Function (ACF)
- •2.1.3 Running Autocorrelation
- •2.2 Physical Factors of Sound Fields
- •2.2.1 Sound Transmission from a Point Source through a Room to the Listener
- •2.2.2 Temporal-Monaural Factors
- •2.2.3 Spatial-Binaural Factors
- •2.3 Simulation of a Sound Field in an Anechoic Enclosure
- •3 Subjective Preferences for Sound Fields
- •3.2.1 Optimal Listening Level (LL)
- •3.2.4 Optimal Magnitude of Interaural Crosscorrelation (IACC)
- •3.3 Theory of Subjective Preferences for Sound Fields
- •3.4 Evaluation of Boston Symphony Hall Based on Temporal and Spatial Factors
- •4.1.1 Brainstem Response Correlates of Sound Direction in the Horizontal Plane
- •4.1.2 Brainstem Response Correlates of Listening Level (LL) and Interaural Crosscorrelation Magnitude (IACC)
- •4.1.3 Remarks
- •4.2.2 Hemispheric Lateralization Related to Spatial Aspects of Sound
- •4.2.3 Response Latency Correlates of Subjective Preference
- •4.3 Electroencephalographic (EEG) Correlates of Subjective Preference
- •4.3.3 EEG Correlates of Interaural Correlation Magnitude (IACC) Changes
- •4.4.1 Preferences and the Persistence of Alpha Rhythms
- •4.4.2 Preferences and the Spatial Extent of Alpha Rhythms
- •4.4.3 Alpha Rhythm Correlates of Annoyance
- •5.1 Signal Processing Model of the Human Auditory System
- •5.1.1 Summary of Neural Evidence
- •5.1.1.1 Physical Characteristics of the Ear
- •5.1.1.2 Left and Right Auditory Brainstem Responses (ABRs)
- •5.1.1.3 Left and Right Hemisphere Slow Vertex Responses (SVRs)
- •5.1.1.4 Left and Right Hemisphere EEG Responses
- •5.1.1.5 Left and Right Hemisphere MEG Responses
- •5.1.2 Auditory Signal Processing Model
- •5.2 Temporal Factors Extracted from Autocorrelations of Sound Signals
- •5.3 Auditory Temporal Window for Autocorrelation Processing
- •5.5 Auditory Temporal Window for Binaural Processing
- •5.6 Hemispheric Specialization for Spatial Attributes of Sound Fields
- •6 Temporal Sensations of the Sound Signal
- •6.1 Combinations of Temporal and Spatial Sensations
- •6.2 Pitch of Complex Tones and Multiband Noise
- •6.2.1 Perception of the Low Pitch of Complex Tones
- •6.2.3 Frequency Limits of Missing Fundamentals
- •6.3 Beats Induced by Dual Missing Fundamentals
- •6.4 Loudness
- •6.4.1 Loudness of Sharply Filtered Noise
- •6.4.2 Loudness of Complex Noise
- •6.6 Timbre of an Electric Guitar Sound with Distortion
- •6.6.3 Concluding Remarks
- •7 Spatial Sensations of Binaural Signals
- •7.1 Sound Localization
- •7.1.1 Cues of Localization in the Horizontal Plane
- •7.1.2 Cues of Localization in the Median Plane
- •7.2 Apparent Source Width (ASW)
- •7.2.1 Apparent Width of Bandpass Noise
- •7.2.2 Apparent Width of Multiband Noise
- •7.3 Subjective Diffuseness
- •8.1 Pitches of Piano Notes
- •8.2 Design Studies of Concert Halls as Public Spaces
- •8.2.1 Genetic Algorithms (GAs) for Shape Optimization
- •8.2.2 Two Actual Designs: Kirishima and Tsuyama
- •8.3 Individualized Seat Selection Systems for Enhancing Aural Experience
- •8.3.1 A Seat Selection System
- •8.3.2 Individual Subjective Preference
- •8.3.3 Distributions of Listener Preferences
- •8.5 Concert Hall as Musical Instrument
- •8.5.1 Composing with the Hall in Mind: Matching Music and Reverberation
- •8.5.2 Expanding the Musical Image: Spatial Expression and Apparent Source Width
- •8.5.3 Enveloping Music: Spatial Expression and Musical Dynamics
- •8.6 Performing in a Hall: Blending Musical Performances with Sound Fields
- •8.6.1 Choosing a Performing Position on the Stage
- •8.6.2 Performance Adjustments that Optimize Temporal Factors
- •8.6.3 Towards Future Integration of Composition, Performance and Hall Acoustics
- •9.1 Effects of Temporal Factors on Speech Reception
- •9.2 Effects of Spatial Factors on Speech Reception
- •9.3 Effects of Sound Fields on Perceptual Dissimilarity
- •9.3.1 Perceptual Distance due to Temporal Factors
- •9.3.2 Perceptual Distance due to Spatial Factors
- •10.1 Method of Noise Measurement
- •10.2 Aircraft Noise
- •10.3 Flushing Toilet Noise
- •11.1 Noise Annoyance in Relation to Temporal Factors
- •11.1.1 Annoyance of Band-Pass Noise
- •11.2.1 Experiment 1: Effects of SPL and IACC Fluctuations
- •11.2.2 Experiment 2: Effects of Sound Movement
- •11.3 Effects of Noise and Music on Children
- •12 Introduction to Visual Sensations
- •13 Temporal and Spatial Sensations in Vision
- •13.1 Temporal Sensations of Flickering Light
- •13.1.1 Conclusions
- •13.2 Spatial Sensations
- •14 Subjective Preferences in Vision
- •14.1 Subjective Preferences for Flickering Lights
- •14.2 Subjective Preferences for Oscillatory Movements
- •14.3 Subjective Preferences for Texture
- •14.3.1 Preferred Regularity of Texture
- •15.1 EEG Correlates of Preferences for Flickering Lights
- •15.1.1 Persistence of Alpha Rhythms
- •15.1.2 Spatial Extent of Alpha Rhythms
- •15.2 MEG Correlates of Preferences for Flickering Lights
- •15.2.1 MEG Correlates of Sinusoidal Flicker
- •15.2.2 MEG Correlates of Fluctuating Flicker Rates
- •15.3 EEG Correlates of Preferences for Oscillatory Movements
- •15.4 Hemispheric Specializations in Vision
- •16 Summary of Auditory and Visual Sensations
- •16.1 Auditory Sensations
- •16.1.1 Auditory Temporal Sensations
- •16.1.2 Auditory Spatial Sensations
- •16.1.3 Auditory Subjective Preferences
- •16.1.4 Effects of Noise on Tasks and Annoyance
- •16.2.1 Temporal and Spatial Sensations in Vision
- •16.2.2 Visual Subjective Preferences
- •References
- •Glossary of Symbols
- •Abbreviations
- •Author Index
- •Subject Index
228 |
11 Applications (IV) – Noise Annoyance |
The results of the two experiments lead to the following conclusions:
1.Moving spatial sound sensations were always more annoying than fixed sound localization under the condition of a constant SPL. The annoyance increased with a greater fluctuation rate of the IACC as well as the τIACC.
2.Fluctuations of the IACC and the SPL independently contribute to the scale value of annoyance.
3.Fluctuations of the τIACC and the SPL independently contribute to the scale value of annoyance.
4.The contribution of fluctuations in τIACC to annoyance was greater than that of the IACC when the range of the SPL was from 65 to 75 dBA.
Therefore, in order to describe subjective evaluations of moving noise sources, we should make binaural measurements to obtain both the spatial factor extracted from the IACF and the temporal factor extracted from the ACF.
11.3 Effects of Noise and Music on Children
We sought to understand the differential effects of noise and music on the performance of mental tasks by children. Under conditions of quiet (no stimulus), noise, and music, children carried out cognitive tasks that are thought to lateralized in one hemisphere or the other (Table 11.5). The tasks either involved addition (left hemisphere specialized) or pattern search (right hemisphere specialized).
Tests were carried out in classrooms (the reverberation time 0.5–0.9 s in the 500Hz octave band) of two schools in a quiet living area (Ando et al., 1975; Ando and Kang, 1987; Ando, 1988). The total number of subjects participated in the experiments was 559 (Table 11.5). The no-stimulus, quiet condition was tested in a normal classroom without any reproduced sound. The noise group was tested while being exposed to jet plane noise of 95 ± 5 dBA, peak. The music group was tested while listening to an excerpt of music from the fourth movement of Beethoven’s Ninth Symphony (85 ± 5 dBA, peak). As shown in Fig. 11.12, the time pattern of the
Table 11.5 Number of subjects monitored while performing two different metal tasks
Task |
Age(years) |
No-stimulus group |
Noise group |
Music group |
Total |
|
|
|
|
|
|
Addition |
|
|
|
|
|
(Left-hemisphere |
9–10 |
120 |
123 |
36 |
279 |
task) |
|
|
|
|
|
Patterns search |
|
|
|
|
|
(Right-hemisphere |
7–8 |
123 |
119 |
38 |
280 |
task) |
|
|
|
|
|
Total subjects |
|
243 |
242 |
74 |
559 |
|
|
|
|
|
|
11.3 Effects of Noise and Music on Children |
229 |
Fig. 11.12 Sound-pressure levels of stimuli reproduced in classrooms as a function of time. Left: Aircraft noise adjusted by a peak of 90 dBA in this figure. Right: Music piece before the chorus of Beethoven’s Ninth Symphony adjusted by a peak of 90 dBA
music was similar to that of the jet noise. The spectra of the two sound signals were similar also (Ando et al., 1975). Music and aircraft noise were reproduced from two loudspeakers set at the front of the classroom, during every alternative period during the addition and search tasks given by
i = 2n |
(11.6) |
where n = 1, 2, . . ., 7 for the adding task, and n = 1, 2, . . ., 5 for the search task. Examples of one task period are shown in the upper part of Fig. 11.13 (60 s/period)
Fig. 11.13 Proportion of V-type relaxed children during the adding task (left-hemispheric task) without any stimuli, with aircraft noise stimulus reproduced and music stimulus reproduced. The upper part indicates the task of one period (60 s) in
N = 15
230 |
11 Applications (IV) – Noise Annoyance |
Fig. 11.14 Proportion of V-type relaxed children during the search task (right-hemispheric task) without any stimuli, with aircraft noise stimulus reproduced and music stimulus reproduced. The upper part indicates the task of one period (30 s) in
N = 10
and Fig. 11.14 (30 s/period). The individual work produced in each period, called the “working curve,” was drawn for all test results. The mean work performance is not discussed here, because there were no significant differences between the different conditions. Of particular importance in evaluating the tests results is the “V-type relaxation.” This score is classified into two categories according to the occurrence of a sudden large fall in the working curve during each task. This is assessed by Mi < M – (3/2)W, i = 1, 2, . . ., N, where M is the mean work performance and W is the average variation of the curve excluding an initial effect at the first period, i = 1. Such relaxation is thought to be caused by an abandonment of effort when mental functions are disturbed.
As shown in Fig. 11.13, the percentage of V-type relaxed children given the additional task (N = 15) was much greater in the music group than in either the no-stimulus group or the noise group (p < 0.01). As shown in Fig. 11.14 for patternsearch task (N = 10), the percentage of relaxed children was similar under all test conditions, except for a slight increase in the noise group. The results of the mental tasks were not dependent on the sex, birth order, or birth weight of a child or on whether or not the mother was a working mother (Ando et al., 1975).
Significant differences in the factors τ1 and τe extracted from the running ACF of the noise and the music as a function of time may be found in measured results
11.3 Effects of Noise and Music on Children |
231 |
(Ando, 2001b). Because of the central auditory signal-processing model (Fig. 5.1), these temporal factors may stimulate the left hemisphere activated by the fluctuation of these temporal factors. It is worth noting that the τe value is deeply related to the most preferred temporal factors ( t1 and Tsub) of the sound field as expressed by Equations (3.3) and (3.4), which are associated with the left hemisphere (Table 5.1).
Effects of temporary music and noise stimuli on mental tasks were closely related to the content of the task being performed or to specialization of cerebral hemispheres. In the case of the addition task, there were no significant differences between the noise group and the no-stimulus group in the percentage of V-type relaxed children. This may support the theory that noise and calculation tasks are separately processed in the right and left hemispheres, respectively (Ando, 1988). Thus as illustrated in Fig. 11.15, no interference effects of the noise were evident in the adding task. However, the percentage of relaxed children in the music stimulus group differs significantly from that in the noise group and the no-stimulus group. This may be explained as an interference effect in the left hemisphere – music perception and calculation being processed sequentially in this hemisphere. On the other hand, music perception as a sequence of time and the spatial pattern task (search task) may be independently processed in the left and right hemispheres, respectively. In the search task, therefore, although no significant differences in the number of V-type relaxed children could be observed under the no-stimulus and music conditions, a difference was observed (p < 0.1), so that interference of the noise and the search task in the right hemisphere seems to be discernible (Fig. 11.15).
Fig. 11.15 Explanations of interference effects between mental tasks and sound stimuli by mean of the specialization of cerebral hemispheres. Aircraft noise (with less fluctuation of τe) and music (with a greater fluctuation τe), respectively, may be associated mainly with the right hemisphere and the left hemisphere. The adding task and search task, respectively, may be associated mainly with the left hemisphere and the right hemisphere
232 |
11 Applications (IV) – Noise Annoyance |
Differences in interference effects occurring during left and right hemispheric tasks, respectively, may be well described in terms of the temporal factors extracted from ACF and the spatial factors extracted from IACF as listed in Table 11.6. On the other hand, annoyance may be described by all of these factors.
Table 11.6 Effects of noise on two difference tasks and annoyance, in relation to the temporal and spatial factors extracted from the ACF and the IACF, respectively
Factors |
|
Effects of noise on two different tasks and annoyance |
|
|
|
|
Left-hemispheric task Right-hemispheric |
Annoyance |
|
|
|
task |
|
|
|
|
|
|
|
ACF |
|
|
|
|
|
τ1 |
X |
X |
|
|
φ1 |
X |
X |
|
|
τe |
X |
X |
|
IACF |
LL |
X |
X |
|
|
τIACC |
X |
X |
|
|
WIACC |
X |
X |
|
|
IACC |
X |
X |
|
X: Factors may influence the corresponding task, and annoyance. LL = 10 log [ (0)/ (0)ref], where (0) = [ ll(0) rr(0)]1/2.
