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Convention Paper Presented at the 138th Convention 2015 May 7–10 Warsaw, Poland This paper was peer-reviewed as a complete manuscript for presentation at this Convention. Additional papers may be obtained by sending request and remittance to Audio Engineering Society, 60 East 42nd Street, New York, New York 10165-2520, USA; also see www.aes.org. All rights reserved. Reproduction of this paper, or any portion thereof, is not permitted without direct permission from the Journal of the Audio Engineering Society.

Psychoacoustic Annoyance Monitoring with WASN for Assessment in Urban Areas Jaume Segura1 , Santiago Felici1 , Maximo Cobos1 , Ana Torres2 and Juan M. Navarro3 1

Computer Science Dpt, ETSE - Universitat de Valencia, Burjassot, 46100, SPAIN

2

Polytechnic University School of Cuenca, Campus Universitario, Cuenca, 16071, SPAIN

3

Universidad Catolica San Antonio - Murcia, Campus de los Jernimos s/n, Guadalupe (Murcia), 30107, SPAIN

Correspondence should be addressed to Jaume Segura ([email protected]) ABSTRACT The assessment of the subjective annoyance caused by noise pollution in cities is a matter of major importance as its influence is growing-up in urban areas. Different schools have made approaches to model this subjective annoyance in terms of several psychoacoustic parameters which defines different aspects of acoustic affection of noise pollution in human behavior. In this paper, we describe the implementation of some binaural algorithms to compute the psychoacoustic metrics following the theories of the Munich School to evaluate the annoyance. These algorithms have been integrated into a SoC platform to work on a networked system (WASN) evaluating the subjective annoyance in an urban area.

1. INTRODUCTION Noise pollution affects most of the population living in urban areas. European Commission has considered noise as a major issue, adopting the END [1], which recommends countries to establish policies of noise monitoring and control to develop local action plans. Road traffic noise and subjective annoyance has been studied in [2]. The authors provide a review of the annoyance from the road traffic noise and analyze its main features and frequency spectrum (between 20 Hz and 8 kHz). The authors measure the annoy-

ance using two different methods, the first is by the means of manual surveys to the citizens, while the second is by using the day-night average sound level to estimate the percentage of highly annoyed from the psychological point of view. In [3], the Zwicker’s annoyance model [13] is used for soundscape categorization to determine how an acoustic environment sounds like, using manually collected noise samples. Several works have already considered the use of Wireless Acoustic Sensor Networks (WASN) for noise monitoring. In [4] and [5], the authors evaluate a WASN based on two different motes, Tmote-Sky

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Annoyance Monitoring with WASN

[6] and Tmote Invent, to monitor road traffic noise measured by the Leq,T [4] with 8 kHz sampling frequency and for counting the number and type of vehicles. They conclude that the Tmote-Sky has excessive self-noise and the Tmote Invent (with on-board microphone) has apparently good audio features. In these references, the authors do not provide any calibration. In [7] and [8] show a WASN deployed in Ostrobothnia (Finland), reporting different tests to evaluate the noise impact. The authors measure the Leq,T with T = 125ms using a sampling frequency of 33 kHz, with 14 calibrated motes (MicaZ from Crossbow/MEMSIC with an ad-hoc acquisition circuitry to allow a dynamic range of 60 dB), globally synchronized during 96 hours with good results. Other references such as [9][11] use the mobile phones for noise pollution monitoring. Although the results are interesting, in our opinion the lack of information about the recording conditions prevents getting accurate noise measurements. When assessing noise indicators, the location of the measuring devices must follow defined rules [1]. The application of Wireless Acoustic Sensor Networks (WASN) allows higher levels of granularity at the spatial monitoring level. Each node has its own power supply, processing unit and memory. The nodes communicate using multi-hop routing protocols and at least one node (called sink) acts as a gateway for external connection. Nevertheless, these networks and their applications are still far from being mature, due to the constraints on resources such as energy, memory, computational speed and communications bandwidth. Actually, there are some projects developing this networked monitoring [4][5][10][11], gathering equivalent sound pressure level over time (Leq,T), but these measurements fail to provide enough information related to subjective annoyance [3]. This paper aims to analyze the performance of WASN to evaluate the subjective annoyance based on the measurement and computation of different psychoacoustic metrics according to annoyance model of the Munich school [13]. To this end, low cost WASNs using SoC sensor nodes are considered, in particular Raspberry Pi (RPi) platforms. This model measures the Nuisance (N) based on other parameters which are: Loudness (L), Sharpness (S), Roughness (R) and Fluctuation Strength (F). The

Fig. 1: RPi connected to the sound card and the microphone.

nodes sample the noise, estimate N (measuring L, S, R and F) and finally send the results in a data packet to the sink through the network [14]. 2. METHODOLOGY The application of established algorithms to compute psychoacoustic metrics for binaural perception [15] allows to calculate the subjective annoyance. These algorithms have been implemented on a System-on-Chip (SoC) platform connected to a soundcard. Figure 1 shows the RPi connected to the sound card and the microphone. Table 1 contains information about the expression to calculate the different psychoacoustic metrics involved in the computation of the subjective annoyance.

AES 138th Convention, Warsaw, Poland, 2015 May 7–10 Page 2 of 5

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L=


P24Bark z=0

L0 (z) · ∆z

P24Bark

L0 (z)·e0.171·z ·z·∆z L

S = 0.11 ·

z=0

R = 0.0003 ·

P24Bark

F = 0.032 ·

z=0

P24Bark z=0

fmod (z) · ∆LE (z) · ∆z

∆LE (z)·∆z fmod (z) + f 4 (z) 4 mod

Table 1: Numerical expressions for L, S, R and F 3. RESULTS AND DISCUSSION The SoC platform used is a Raspberry Pi (RPi) platform which is based on Broadcom BCM2835 SoC, including an ARM1176JZF-S 700 MHz processor, a GPU and 512 MB of RAM, with a SD slot card memory. We installed in the RPis a Logilink UA0053 USB sound card, with an electret omnidirectional microphone and a WiFi adapter TPLink TL-WN725N with IEEE 802.11 b/g/n standard. The RPi have been placed in protective housings, each with 3 Kodak 1.5V KD LR20 batteries (Figure 1).

Fig. 2: Detail of the vertical and horizontal network deployment at the outskirts of Valencia city, exit to Barcelona (Highway A7).

Before setting up the network test, we performed a standard calibration using piston phones. The measurements were compared with a Type I standard sound level meter (CESVA SC-310) and showed a less than ±2dB error in both short-term and longterm. The network test was carried out in a horizontal deployment near “Torre Miramar”, close to the A-7 highway in a traffic congested area of Valencia City (Figure 2). We measured every 20 meters over a distance of 290 meters from the bottom of this tower. We evaluated the subjective annoyance following the Zwicker’s annoyance model (Munich school) [13]. Figure 3 shows the mean values and the standard deviation of the annoyance calculated from the psychoacoustic measurements taken by the RPi network.

Fig. 3: Annoyance measurements in the horizontal network deployment with the RPi.

We have also made noise measurements with the standard sound level meter[12] and we have obtained


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to the assessment and management of environmental noise”, Off. J. Eur. Communities, vol. L189, pp. 1225, Jul. 2002. [2] D. Ouis, “Annoyance from road traffic noise: A review”, J. Environ.Psychol., vol. 21, no. 1, pp. 101120, 2001. [3] M. Rychtarikova and G. Vermeir, “Soundscape categorization on the basis of objective acoustical parameters”, Appl. Acoust., vol. 74, no. 2, pp. 240247, Feb. 2013.

Fig. 4: Analysis of the correlation coefficient (ρ) between Leq,A and the psycho-acoustic parameters (A, L, S, R, F) at each node location. correlations between LeqA and psycho-acoustic metrics. These results are shown in Figure 4. 4. CONCLUSIONS In this paper, a set of experiences are reported in using the RPi platforms for collecting road traffic noise pollution data in outdoor environments and measuring the subjective annoyance, and pointed out the potentials and limitations of these two hardware alternatives. While the results showed the feasibility of WASN to be used as noise pollution sensors, in particular based on the RPi platforms, they also illustrated the practical limitations of todays commercial and off-the shelf available platforms. We explored the interest for the psycho-acoustic parameters and their additional information related to the road traffic noise monitoring, by a correlation analysis with the LeqA,10s . We found that for road traffic noise, the LeqA,10s is correlated slightly with N and L, but not with S, R and F. This confirms that the Zwicker’s model provides more information than the measurements based on the equivalent noise pressure level, in particular LeqA,10s , to assess the subjective annoyance. 5. REFERENCES [1] “Directive 2002/49/EC of the European parliament and of the council of 25 june 2002 relating

[4] S. Santini and A. Vitaletti,“Wireless sensor networks for environmental noise monitoring”, in Proc. 6th GI/ITG KuVS Workshop Wireless Sensor Netw., Aachen, Germany, Jul. 2007, pp. 98101. [5] S. Santini, B. Ostermaier, and A. Vitaletti, “First experiences using wireless sensor networks for noise pollution monitoring”, in Proc.3rd ACM Workshop Real-World Wireless Sensor Netw. (REALWSN), Glasgow, U.K., Apr. 2008, pp. 6165. [6] J. Polastre, R. Szewczyk, and D. Culler, “Telos: Enabling ultra-low power wireless research”, in Proc. 4th Int. Symp. Inf. Process. Sensor Netw. (IPSN), Apr. 2005, pp. 364369. [7] I. Hakala, I. Kivela, J. Ihalainen, J. Luomala, and C. Gao, “Design of low-cost noise measurement sensor network: Sensor function design”, in Proc. IEEE 1st Int. Conf. Sensor Device Technol. Appl. (SensorDevices), Jul. 2010, pp. 172179. [8] I. Kivel, C. Gao J. Luomala, and I. Hakala, “Design of noise measurement sensor network: Networking and communication part”, in Proc. 5th Int. Conf. Sensor Technol. Appl., 2011, pp. 280287. [9] S. Santini, B. Ostermaier, and R. Adelmann, “On the use of sensor nodes and mobile phones for the assessment of noise pollution levels in urban environments”, in Proc. 6th Int. Conf. Netw. Sensing Syst. (INSS), Pittsburgh, PA, USA, Jun. 2009, pp. 3138.


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