- •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
Chapter 8
Applications (I) – Music and Concert Hall
Acoustics
This chapter offers some applications of the central auditory signal processing model for temporal and spatial sensations as well as for subjective preferences. The measurement of pitches of notes sounded by a piano are discussed in Section 8.1. Examples of adaptive acoustic design of a public concert hall using global listener preference data are presented in Section 8.2. A seat selection system for enhancing individual listening experiences in a concert hall is discussed in Section 8.3. The preferred temporal conditions for music performance by cellists are discussed as it relates to acoustic design of the stage in Section 8.4.
8.1 Pitches of Piano Notes
It is well known that the source signal of a piano is a complex tone with mostly low-frequency harmonics. According to the method described in Section 6.2, source signals of pianos were analyzed and compared with those calculated using ratios of
neighboring notes in an equally tempered chromatic scale, i.e., semitone steps of
21/12.
For this study, source signals were picked up by a single microphone placed at the center position above a grand piano with its top lid opened at the usual angle for performance (Inoue and Ando, unpublished). The piano was equipped with an automatic performance system, was tuned before measurement, and produced source signals that could be reliably reproduced. Examples of the ACF analyzed for notes A1 (55 Hz), A3 (220 Hz), and A6 (1760 Hz) are shown in Fig. 8.1. It is clear that the delay times of the first peak τ 1 extracted from the ACF correspond well to the measured fundamental frequencies of notes A1 (55.2 Hz), A3 (219.7 Hz), and A6 (1785.7 Hz). Its amplitude φ1 is large enough (more than 0.8) and in this condition, a clear pitch is perceived. Calculated and measured pitches for all of 88 notes are shown in Figs. 8.1 and 8.2, and these values are listed in Table 8.1.
Y. Ando, P. Cariani (Guest ed.), Auditory and Visual Sensations, |
143 |
DOI 10.1007/b13253_8, C Springer Science+Business Media, LLC 2009 |
|
144 |
8 Applications (I) – Music and Concert Hall Acoustics |
Fig. 8.1 Examples of the ACF analyzed for source signals from a piano. (a) Note A1 of the pitch of 55 Hz. (b) Note A3 of the pitch of 220 Hz. (c) Note A6 of the pitch of 1760 Hz. The missing fundamental phenomena may be observed by τ1 extracted from the ACF, which corresponds to 55 Hz and 220 Hz. However, the pitch of 1760 Hz is observed by τ1 at just the fundamental frequency
8.1 Pitches of Piano Notes |
145 |
Fig. 8.2 Relationship between calculated and measured pitches obtained by the value of τ1 extracted from the ACF of source signals from a “tuned” piano
Most of the |
measured |
values are in good agreement with calculated ones. |
It is interesting, |
however, |
that measured pitches below G3 (207.6 Hz) were a |
little bit higher than calculated ones. Below this pitch, the amplitudes of the fundamental frequency component in the spectrum analyzed were small and not significant, and thus the low pitch that one hears from these piano notes is primarily a missing fundamental phenomenon. For pitches below 55 Hz, no appreciable energy at the fundamental frequency in the measured spectrum was observed.
It is worth noting that the upper frequency limit of the ACF model for the pitches of missing fundamentals is about 1,200 Hz (Section 6.2.3). Above the frequency of this pitch, discrepancies between calculated and measured pitches grew large, reaching about 2%. At G7 (3135.9 Hz), in particular, a large discrepancy was observed, where a measured value of 1612.9 Hz corresponded roughly to half the calculated one. The octave error could conceivably have been caused by a mistake in tuning adjustment or, perhaps more likely, in the autocorrelation analysis (picking the second major peak rather than the first one). In some neural autocorrelation models (Cariani, 2004), this problem is largely avoided by analyzing the ACF for regular patterns of major interspike interval peaks rather than choosing the highest peak. The method uses a dense set of interval sieves that quantify the pattern strengths of all possible periodicities, which allows the model to estimate the relative strengths of multiple, competing pitches that may be heard in a given note or chord.
146 |
8 Applications (I) – Music and Concert Hall Acoustics |
Table 8.1 Calculated pitches and the values measured by the ACF (τ1) of the 88-note signals for a piano that was said to be “tuned”
Note |
Calculated pitch (Hz) |
Measured pitch (Hz) |
Difference (Hz) |
|
|
|
|
A0 |
27.5 |
29.9 |
−2.4 |
B0 |
29.1 |
31.5 |
−2.4 |
H0 |
30.8 |
33.5 |
−2.7 |
C1 |
32.7 |
35.1 |
−2.4 |
Cis1 |
34.6 |
34.8 |
−0.2 |
D1 |
36.7 |
36.9 |
−0.2 |
Es1 |
38.8 |
39.2 |
−0.4 |
E1 |
41.2 |
41.4 |
−0.2 |
F1 |
43.6 |
43.6 |
0.0 |
Fis1 |
46.2 |
46.5 |
−0.3 |
G1 |
48.9 |
49.2 |
−0.3 |
Gis1 |
51.9 |
52.3 |
−0.4 |
A1 |
55.0 |
55.2 |
−0.2 |
B1 |
58.2 |
58.8 |
−0.6 |
H1 |
61.7 |
62.1 |
−0.4 |
C2 |
65.4 |
65.7 |
−0.3 |
Cis2 |
69.2 |
69.4 |
−0.2 |
D2 |
73.4 |
74.0 |
−0.6 |
Es2 |
77.7 |
78.1 |
−0.4 |
E2 |
82.4 |
82.6 |
−0.2 |
F2 |
87.3 |
87.7 |
−0.4 |
Fis2 |
92.4 |
92.5 |
−0.1 |
G2 |
97.9 |
98.0 |
−0.1 |
Gis2 |
103.8 |
104.1 |
−0.3 |
A2 |
110.0 |
111.1 |
−1.1 |
B2 |
116.5 |
117.6 |
−1.1 |
H2 |
123.4 |
124.2 |
−0.8 |
C3 |
130.8 |
131.5 |
−0.7 |
Cis3 |
138.5 |
138.8 |
−0.3 |
D3 |
146.8 |
149.2 |
−2.4 |
Es3 |
155.5 |
156.2 |
−0.7 |
E3 |
164.8 |
165.2 |
−0.4 |
F3 |
174.6 |
175.4 |
−0.8 |
Fis3 |
184.9 |
185.1 |
−0.2 |
G3 |
195.9 |
196.0 |
−0.1 |
Gis3 |
207.6 |
208.3 |
−0.7 |
A3 |
220.0 |
219.7 |
+0.3 |
B3 |
233.0 |
232.5 |
+0.5 |
H3 |
246.9 |
246.9 |
0.0 |
C4 |
261.6 |
263.1 |
−1.5 |
Cis4 |
277.1 |
277.7 |
−0.6 |
D4 |
293.6 |
294.1 |
−0.5 |
Es4 |
311.1 |
312.5 |
−1.4 |
E4 |
329.6 |
327.8 |
+1.8 |
8.1 Pitches of Piano Notes |
147 |
|
Table 8.1 |
(continued) |
|
|
|
|
|
Note |
Calculated pitch (Hz) |
Measured pitch (Hz) |
Difference (Hz) |
|
|
|
|
F4 |
349.2 |
350.8 |
−1.6 |
Fis4 |
369.9 |
370.3 |
−0.4 |
G4 |
391.9 |
393.7 |
−1.8 |
Gis4 |
415.3 |
416.6 |
−1.3 |
A4 |
440.0 |
444.4 |
−4.4 |
B4 |
466.1 |
465.1 |
+1.0 |
H4 |
493.8 |
495.0 |
−1.2 |
C5 |
523.2 |
526.3 |
−3.1 |
Cis5 |
554.3 |
558.6 |
−4.3 |
D5 |
587.3 |
591.7 |
−4.4 |
Es5 |
622.2 |
625.0 |
−2.8 |
E5 |
659.2 |
666.6 |
−7.4 |
F5 |
698.4 |
704.2 |
−5.8 |
Fis5 |
739.9 |
740.7 |
−0.8 |
G5 |
783.9 |
787.4 |
−3.5 |
Gis5 |
830.6 |
826.4 |
+4.2 |
A5 |
880.0 |
892.8 |
−12.8 |
B5 |
932.3 |
943.3 |
−11.0 |
H5 |
987.7 |
980.3 |
+7.4 |
C6 |
1046.5 |
1041.6 |
+4.9 |
Cis6 |
1108.7 |
1111.1 |
−2.4 |
D6 |
1174.6 |
1176.4 |
−1.8 |
Es6 |
1244.5 |
1265.8 |
−21.3 |
E6 |
1318.5 |
1333.3 |
−14.8 |
F6 |
1396.9 |
1408.4 |
−11.5 |
Fis6 |
1479.9 |
1492.5 |
−12.6 |
G6 |
1567.9 |
1538.4 |
+29.5 |
Gis6 |
1661.2 |
1666.6 |
−5.4 |
A6 |
1760.0 |
1785.7 |
−25.7 |
B6 |
1864.6 |
1851.8 |
+12.8 |
H6 |
1975.5 |
2000.0 |
−24.5 |
C7 |
2093.0 |
2083.3 |
+9.7 |
Cis7 |
2217.4 |
2272.7 |
−55.3 |
D7 |
2349.3 |
2380.9 |
−31.6 |
Es7 |
2489.0 |
2500.0 |
−11.0 |
E7 |
2637.0 |
2631.5 |
+5.5 |
F7 |
2793.8 |
2857.1 |
−63.3 |
Fis7 |
2959.9 |
3030.3 |
−70.4 |
G7 |
3135.9 |
1612.9 |
+1523.0 |
Gis7 |
3322.4 |
3448.2 |
−125.8 |
A7 |
3520.0 |
3703.7 |
−183.7 |
B7 |
3729.3 |
3703.7 |
+25.6 |
H7 |
3951.0 |
4000.0 |
−49.0 |
C8 |
4186.0 |
4347.8 |
−161.8 |
A little less than one octave mistuned.
