Chapter 10
Applications (III) – Noise Measurement
The central auditory signal processing model lends itself to a wide range of applications. In this chapter, we will first discuss a method of measuring identificaiton and subjective evaluation of environmental noise. Then, examples of noise measurement are discussed in terms of both the temporal factors extracted from the ACF of the source signal and spatial factors extracted from the IACF.
10.1 Method of Noise Measurement
A method of measuring the noise for its identification and subjective evaluations of the noise based on the model of the central auditory system are described in this section (Ando, 2001a; Ando and Pompoli, 2002). As is discussed in Chapters 5 through 7, temporal sensations are described by the three temporal factors extracted from the ACF, and spatial sensations are described by the four spatial factors from the IACF. Since the temporal and spatial factors may be dominantly processed in the left and right hemisphere, respectively, the two main factors may independently contribute to judgments of any subjective responses of noise. Thus, any of subjective evaluation S of a noise signal may be expressed essentially by Equation (6.6), so that,
S = SL + SR = fL(τ1, φ1, τe) + fR(LL, IACC, WIACC, τIACC) |
(10.1) |
where SL = fL(τ1, φ1, τe) and SR = f R(LL, IACC, WIACC, τIACC); both temporal factors and spatial factors are defined in Chapter 5 and formulated in Section 6.1. As indicated in Table 10.1a, temporal sensations and spatial sensations and identification of noise sources are described by these measured temporal and spatial factors, respectively. When WIACC is taken into account, then the factor Wφ(0) may be eliminated, due to a large correlation between them. Computer software for the identification of noise sources and noise measurement has been discussed based on Equation (10.1) (Sakurai et al., 2001; Sakai et al., 2001; Sakai et al., 2002; Fujii et al., 2004a). It is worth noting that the previous envelope-based method for the sound level meter (SLM) often does not well describe subjective responses (Table 10.1b).
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Table 10.1 Comparison between the previous envelope-based method measured by use of the sound-level meter and the correlation method proposed here
(a) Physical factors to be measured
Temporal and spatial factors |
Envelope method |
Correlations method |
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|
(1) |
SPL |
Envelope SPL (dBA) |
ll(0), rr(0), LL, or binaural |
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SPL (dBA) |
|
(2) |
Running temporal window (2T) |
Fast, slow, and peak hold |
Adaptive; it is about 30(τe)min |
|
(3) |
τ1 |
Impossible |
Measurable |
|
(4) |
φ1 |
Impossible |
Measurable |
|
(5) |
τe and (τe)min |
Impossible |
Measurable |
|
(6) |
IACC |
Impossible |
Measurable |
|
(7) |
WIACC |
Impossible |
Measurable |
|
(8) |
τIACC |
Impossible |
Measurable |
|
(b) Temporal and spatial sensations to be related to identify and evaluate the noise source
Subjective responses |
Envelope method |
Correlations method |
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|
(1) |
Loudness |
Partially possible |
fL(τe, τ1, φ1) at constant LL |
(2) |
Pitch or missing fundamental |
Impossible |
fL(τ1, φ1) |
(3) |
Duration sensations |
|
fL(τe, τ1, φ1) |
(4) |
Timbre |
Impossible |
fL(Wφ(0)) |
(5) |
Localization in horizontal plane |
Impossible |
fR( ll(0), rr(0), τIACC, IACC, |
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|
WIACC) |
(6) |
ASW |
Impossible |
fR(LL, WIACC, τIACC, IACC) |
(7) |
Subjective diffuseness |
Impossible |
fR( ll(0), rr(0), IACC, WIACC) |
(8) |
Annoyance |
Impossible |
fL(τe, τ1, φ1) + |
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fR( ll(0), rr(0), τIACC, IACC, |
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WIACC) |
(9) |
Identification of noise sources |
Impossible |
fL(τe, τ1, φ1, Wφ(0)) + |
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fR( ll(0), rr(0), τIACC, IACC, |
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|
WIACC) |
10.2 Aircraft Noise
The acoustic properties of aircraft noise were investigated by means of temporal and spatial factors in a real field. As is described in Chapter 5, from the ACF analysis (1) the sound energy (0), (2) the effective duration of ACF, (τe)min, (3) the delay time of the first peak, τ1, and (4) its amplitude φ1 were extracted. From the IACF analysis, three spatial factors are extracted, (1) the magnitude of the interaural crosscorrelation, IACC, (2) the interaural delay time at IACC, τIACC, and (3) the width of the maximum peak of the IACF, WIACC, as well as the binaural listening level, LL.
This section describes the acoustic properties of aircraft noise in terms of both temporal and spatial factors (Fujii et al., 2001). Aircraft noise causes serious problems such as hearing loss and also has serious effects on the growth of unborn
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babies, infants, and children as discussed later. Much effort has been made in noise research and noise reduction technologies and methods to reduce noise levels. However, it is likely that perceived acoustic properties have not been established sufficiently. In particular, the relationship between physical properties and psychological effects is still unclear. For example, even if a sound exists that has an SPL below standards such as the equivalent sound level (Leq), the effective perceived noise level (EPNL), and the noise and number index (NNI), it can be perceived as much more noisy than expected in a given situation. Such an annoyance may be related to both temporal and spatial factors associated with the left and right cerebral hemispheres, respectively.
Measurements were taken outdoors near the Osaka International Airport (OIA) in 1999 and near the flight course of the Kansai International Airport (KIA) in 2000. Locations of the measurement are illustrated in Fig. 10.1. At the OIA, two locations were chosen close to the runway to measure the noise from aircraft landing and taking off. The distance between the runway and each measuring point was about 100 m. The ambient noise level in this area was higher because of road traffic (60 ± 2 dB). It was cloudy and windless at ground level. The temperature was about 10◦C during the measurement. For measurement of noise along the flight course of the KIA, a dummy head was set near the coast. This location is 20 km southwest of the airport, and the flight course for landing is about 1.0 km from the shore. The altitude of the plane used in the measurement was about 1.0 km above sea level, according to flight data reported from the airport. It was cloudy and windless at ground level during the measurement also. The average temperature for the day was 12◦C. The ambient noise level in this area was 43 ± 2 dB. Noise signals were recorded through two half-inch condenser microphones set at both ear positions of
Fig. 10.1 Locations of two international airports and the measurement points
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a sphere representing a human head. This dummy head was made of 20-mm-thick Styrofoam with a diameter of 200 mm of the sphere. Microphones were set at 1.5 m above the ground.
The duration of the noise depended on the location of the aircraft, its speed, and the distance to the receiving position. The values of (τe)min extracted from its ACF were between 10 ms and 20 ms. Loudness variation was matched with SPL variations when it was analyzed by 2T = 0.25 s or 0.5 s. Thus, it was reconfirmed that the integration interval recommended be 2T ≈ 30 (τe)min as discussed in Section 5.3. In the current study, the integration interval was chosen as 0.25 s for signals with (τe)min ≈ 10 ms and as 0.5 s for signals with (τe)min ≈ 20 ms. As is shown in Fig. 10.2, when 2T was 1 s, it was too long to capture the fluctuation of loudness, but a finer variation of loudness could be observed when 2T = 0.25 s.
Fig. 10.2 Effective duration and temporal integration windows for measurement of aircraft noise. Examples of sound pressure level (SPL) obtained by the geometric mean of ll(0) and rr(0) with a flat filter at the two ears for two different types of noise signals. (a) Noise with (τe)min = 20 ms (2T = 0.5 s). (b) Noise with (τe)min = 10 ms (2T = 0.25 s). The SPL measured by three different integration intervals 2T = 0.25, 0.5, and 1.0 s is shown as a function of time
Typically aircraft produce predominantly high frequency noise while approaching and predominantly low frequency noise after they pass overhead and recede in the distance. Such acoustic characteristics of planes landing are clearly represented
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by factors derived from the ACF (Fig. 10.3 (a)). Measured SPL (= LL) obtained by the geometric mean of ll(0) and rr(0) is shown as a function of time up to 10 s during higher levels than those of other ambient noises. Around when t = 5.0 s, the aircraft had just flown overhead. The delay time and the amplitude of the first peak in ACF, τ1 and φ1, represent the perceived pitch and its strength (see Section 6.2). Results indicate that the perceived pitch varied throughout the flight. A strong tonal component was observed, as the aircraft approached with the value of φ1 being increased up to about 0.5, τ1 ≈ 1.0 ms (1000 Hz). The strongest pitch of 3300 Hz was perceived when the aircraft passed just overhead, at which the value of τ1 was 0.3 ms. After the aircraft passed over, the τ1 value increased and the φ1 value decreased simultaneously due to the Doppler effect after t > 6.5 s, indicating that the noise was dominated by the lower-frequency components. The power spectrum was measured after passing through without any filters, and the ACF was measured after passing through the A-weighting network at t = 1.0, 5.0, and 7.0 s, and these are illustrated in Fig. 10.3b and c, respectively. These show that τ1 and φ1 represent the pitch properties of aircraft noise clearly; at t = 1.0 s, there is a weak peak at τ1 = 1 ms representing a weak pitch of around 1000 Hz, but this information may not be found in the spectrum. At t = 5.0 s, there is a high-frequency component at τ1 = 0.3 ms or 3300 Hz perceived as a tonal sound; and at t = 7.0 s, such a strong peak disappeared and the lower-frequency components increase below 500 Hz, which is perceived like the white noise in the low-frequency range.
Fig. 10.3 Measured factors extracted from the ACF of aircraft noise. (a) The SPL, the value of τ1, and the value φ1 for landing aircraft as a function of time. (b) The power spectra at t = 1.0, 5.0, and 7.0 s. (c) The normalized autocorrelation function NACF at t = 1.0, 5.0, and 7.0 s
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Fig. 10.3 (continued)
Figure 10.4a shows the measured three factors extracted from the ACF for the aircraft during level flying overhead at an altitude of about 1 km. Two typical examples of tonal and nontonal noise analyzed by the power spectra and the normalized ACF, respectively, are shown in Fig. 10.4b and c. The SPLs of the two cases fluctuate in the same manner throughout 10 s, but the values of τ1 and φ1 were extremely different. The mean values of τ1 for the two cases were 3.06 and 2.45 ms, but the φ1 values for two cases were quite different. At times with high φ1, a tonal noise at τ1 = 3.06 s (about 330 Hz) was heard, and its pitch strength fluctuated due to both φ1 and the SPL.
Examples of the measured normalized IACF are shown in Fig. 10.5a for landing, take-off, and two flying conditions. The values of the IACC, τIACC, and WIACC were extracted from the IACF. For example, the measured IACC of four conditions is shown in Fig. 10.5b as a function of time. For the landing and take-off conditions,
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Fig. 10.4 (a) Examples of measured SPL and values of τ1 and φ1 for two cases of flying aircraft as a function of time. (–––): tonal noise; (-----): un-tonal noise. (b) Examples of the measured spectrum at the times when A, B and C indicated in Fig. 10.4(a). (c) Examples of normalized ACF measured at the times A, B, and C indicated in Fig. 10.4a
the IACF had a strong peak at τIACC ≈ 0, meaning that the direction of the noise source is perceived clearly just above in the median plane. When τIACC is positive, the localization of the sound source is in the right-hand side, and when it is negative, then the localization is in the left-hand side. The value of the IACC decreased rapidly when the aircraft passed just overhead before landing, because two jet-noise sources with the high-frequency component arrived from different angles from the median plane (Ando and Sakamoto, 1988; Ando, 1998).
On the other hand, the value of WIACC is dependent on the dominant frequency of the sound. For the take-off condition, WIACC was large, because of the lowfrequency components. The value of IACC was generally small for the level flying condition, so that subjective diffuseness is high. For such a condition, a flying aircraft at high altitude, the sound signal comes from various directions, because various paths of sound rays may exist due to the ground and the climate conditions of the air. Also, the value of WIACC was wider for high-level flying caused by the low-frequency components than for low-level landing and take-off conditions due to the higher-frequency components.
As a typical example, Sakai et al. (2001) have measured τIACC of the noise from aircraft in landing and take-off in Bologna. Figure 10.6 shows measured τIACC in the condition of aircraft running from the left to the right (solid curves) and from the right to the left (dotted curves).
Although it has been reported that the dominant frequency component varied throughout the flight for landing aircraft (e.g., Raney and Cawthon, 1979), the current method of the ACF analysis provides much more precise information about noise properties than does the previous method of the sound-level meter based on the envelope information and the spectra measured by the 1/3 or 1/1 octave-band filters.
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Fig. 10.5 Spatial aspects of aircraft noise. (a) Four measured examples of the normalized IACF. (b) Examples of measured IACC for the conditions of landing (top), take-off (second row), level flying 1 (third row), and level flying 2 (bottom), respectively
Fig. 10.6 Values of τIACC measured near the airport “G. Marconi” in Bologna representing horizontal movement of aircraft in landing and take-off (Sakai et al., 2001)