PROCEEDINGS of the
23rd International Congress on Acoustics
9 to 13 September 2019 in Aachen, Germany

A widened array of metrics (WAM) approach to characterizing urban
sound environments – the example in SALVE
Bryce T. LAWRENCE1; Salman AHMED2; Robynne SUTCLIFFE2; Jonas POPPEN2; Susanne
MOEBUS2; and Dietwald GRUEHN1
1

TU Dortmund University, Germany

2

University of Duisburg-Essen, Germany

ABSTRACTThe emergence of soundscape ecology provided new tools to understand the landscape. Soundscape ecology
studies include numerous sound indices (metrics), based on the factors sound pressure (dB), frequency (Hz)and time. Decibels have been the traditional ‘noise’ measurement worldwide. The authors argue thatutilization of a single factor (dB) has limited value to characterize urban soundscape complexity. Rather,soundscape analysis should address urban soundscapes as holistic compositions of dB and Hz, overspatial-temporal extents, both within and amongst observations. Therefore the MERCUR supported
SALVE project – AcouStic QuAlity and HeaLth in Urban EnVironmEnts - proposes a widened array of
metrics (WAM) approach to enable the broadest possible understanding of the acoustic environment. The
WAM approach will provide a multi-dimensional understanding of sound as signatures, clusters and spatial
disparities of sound phenomena to characterize the sound heterogeneity of urban environments. WAM will be
used to analyze the interrelationship of urban acoustic environments, the built environment and healthoutcomes of urban health studies. This paper presents the metrics included in the WAM approach and therelated software required for calculation.
Keywords: Sound Indices, Acoustic Environment, Soundscapes

1. INTRODUCTION
This paper presents the sound analysis methodology for the project SALVE (AcouStic QuAlity and
HeaLth in Urban EnVironmEnts - Analysis of the interrelationships between sound and health), a two- year interdisciplinary pilot project that started at the end of 2018 and involves public health andspatial planning. SALVE aims to identify criteria for health-promoting acoustic environments, toanalyze associations between human health and acoustic environments in different urban spatial
settings in Bochum, Germany and to develop solutions for a health-promoting spatial and urban
planning approach where a healthy urban soundscape is a component.
As a pilot project, SALVE intends to provide a framework for development of reliable urban sounddata collection and probe a range of relationships between sound, human health and urban spatial
structure. The main objectives are to quantify the acoustic environment for a range of urban land use
types that officially allow human habitation according to German law (Baunutzungsverordnung) anddetermine if objective differences in the acoustic environment or spatial structure can explain
variations in human health outcomes.
By placing the sound analysis emphasis on locations where humans live, SALVE is conceptuallyoriented towards soundscape ecology studies (1). In soundscape ecology, sonotopes (acoustic
environments with homogeneous sound characteristics that can be spatially defined) soundtopes (thealteration of sonotopes due to temporal or spatial configuration) and sonotones (where sonotopes
interface) are defined and evaluated against the effects that patch size, shape, spatial arrangement or
surrounding land use diversity. In SALVE, samples of the urban acoustic environment are analyzed
against natural and man-made spatial configurations to find relationships with species specific(human) health outcomes. Past epidemiology studies have found relationships with decibel levels(noise), urban spatial structure and human health outcomes such as depressive symptoms,
hypertension and sleep disorders (2). SALVE aims to further explore these findings by using the Heinz844

Nixdorf Recall Study (HNR) (3) to analyze human health outcomes with sound and urban spatial
structure.
To achieve the project aims, the sound data collection and analysis package for the project will
expand the definition of sound, from noise to a holistic understanding of decibel and frequencies overspatial-temporal extents, by employing a widened array of metrics (WAM) approach. This paper
presents the results of a rapid literature review used to identify indices for inclusion in WAM anddiscusses their use and calculation platforms.

2. OVERVIEW OF SOUND ANALYSIS WITHIN SALVEAs a pilot project, SALVE intends to develop and deploy the basics for reliable urban acoustic
environment measurement methods. The main objectives of the pilot project are to investigate whether
acoustic environments are representative for a gradient of urban land uses and to determine if those
identified acoustic environments are associated with different human health outcomes.
Health data used in SALVE is from the population-based long-term Heinz Nixdorf Recall (HNR)
study, initiated in the year 1999 (3). The HNR study is a large epidemiological cohort study conductedin the Cities of Bochum, Mülheim and Essen, located in the densely populated Ruhr area in Germany.
The study was designed as an individual based observational study with a sample size of 4814 male s
and females aged 45-74 years. For the purpose of the SALVE study, at least seven health-related
variables - mental health, neighborhood satisfaction, blood pressure, sleep medication intake, smoking
behavior, Body Mass Index (BMI) and comorbidity - will be obtained from the HNR study. The city of
Bochum was chosen as the case study area, given the extent of the HNR study and the proximity of
Bochum to University of Duisburg-Essen and TU Dortmund University.
Sound data collection in SALVE uses devices that stay in the field and follow an automated aural
procedure (AAP) and devices that are carried into the field by researchers and follow a direct aural
procedure (DAP). Given the expense of AAD devices and the time consuming nature of extensive
field data collection with DAP devices, a mix of the AAD and DAP approach is employed in sounddata collection to strategically sample all residentially permitted land use types in Bochum (n = 7). A
total of 24 AAD devices programed to record three minutes samples every 26 minutes (48 observations
per day) are placed stationary for the 12 month data collection period within three samples of eachresidential land use type and one sample of three different forest and open space land use types within
Bochum. A total of 730 DAP sampling locations were chosen for 4 measurements over the 12 month
data collection period (one sample per season) to develop a statistically representative sampling poolof the seven residential land use types, two forest land uses, and one green space land use within
Bochum. This approach provides a temporally detailed but geographically limited AAP baseline oflongitudinal sound data over one year and a temporally limited but geographically detailed sounddataset with the DAP approach to understand how different locations within the study area deviate
from the baseline of AAP recordings (Figure 1).
Spatial proximity of recording points to different features such as highways, city centers, airports ,and availability of surrounding green areas and land use mix, built area index (NDBI), park andrecreation areas, road and rail corridors, urban density gradients (Corine Urban Atlas), natural
ecoregion units, soil and geological units, elevation, physiographic regions and sub -regions,demographic and economic data of the general population, open water and streams, and bus and masstransit stations is also included.
A psychoacoustic analysis of AAD samples according to ISO 12913-2 (4) is included as part of
SALVE to enable analysis of the relationship between calculated metrics from the acoustic
environment and human perceived soundscapes. A ‘soundscape theatre’ at the TU Dortmund
University exposes participants to binaural recordings from AAD locations of the acoustic
environment and participants respond to soundscape questionnaire method A from ISO 12913-2 (4).
In summary, SALVE contains (A) collection of sound data in the city of Bochum, Germany using
both AAD and DAP approaches, (B) collection of a wide range of spatial data to determine the urban
spatial structure in Bochum, (C) development of an automated widened array of metrics (WAM) for
classification of soundscape data into soundscape indices, (D) a psychoacoustic survey according to
DIN ISO 12913-2 to determine soundscape preferences of the acoustic environment in Bochum, and(E) analysis of the association between sound metrics and health outcomes taking into account spatialand socio-demographic data using a range of inferential statistics.

845

Figure 1 – AAD and DAP Sampling Design in SALVE

3. WAM LITERATURE REVIEWThe emergence of soundscape ecology (1, 5) out of the larger landscape ecology (6) concept
framework has given a concrete perspective for understanding the landscape “as a collection of sounds
occurring over an area (7)” or “biological, geophysical, and anthropogenic emanating from alandscape and which vary over time (5).” To accompany this conceptual approach, technical methodsof sound metrics or sound indices, combinations of dB, Hz, and time were also developed and madeavailable via the soundecology package (8) in R (9). While variations of the sound pressure levelparameter alpha index has been extensively reported in the literatu re, including dB(A), Leq, LAeq,
L10, L50, L90 (10, 11, 12, 13), it was unclear as to the total number of sound indices or sound metrics
that had been developed.
With literature reviews already developed through joint seminars and BSc / MSc studio courses at
TU Dortmund and University of Duisburg-Essen since 2017, the project team used a rapid systematic
literature review (14) to develop a summary list of combined dB and Hz sound analysis approaches
within the most recent literature for the development of the WAM approach. The rapid review
searched ScienceDirect and The Journal of Acoustical Society of America for the past decade(2008-2018) using keywords from relevant sound index papers already collected, including Sound,
Soundscape, Acoustic, Urban, Environment, Ecology, Metric, Index in Boolean combinations of AND,OR, and NOT. The raw 10-year literature search found 1,142 studies. The raw abstracts werescreened to find studies that included measurements of environmental sound in English; titles andabstracts were screened to find only studies that included some variation of an index or metric that
employed a dB, Hz and time. The resulting preliminary selection of 57 studies were reviewed for fulltext relevance, and a final selection of 17 studies were used to inform the WAM approach.
Of particular usefulness in summarizing the sound index approach for sound analysis was Sueur (15,16) who presents a broad and detailed overview on the application of sound anal ysis, including 24
sound indices that can be calculated within the program R and its GUI interface R Studio. The total 23

846

soundscape index variations identified include alpha indices (within-group) and beta indices(between-group), where alpha indices describe attributes of different habitats (heterogeneity, evenness,complexity) and beta indices describe dissimilarity and similarity between sound observations (Table1). R Studio offers a streamlined package to data analysis that enables looped calculation of scripts
on large data sets, as is being collected by SALVE (n=493,555 sound recordings), where automateddata analysis is the only practical option.
Table 1 – Alpha and beta sound indices, their origin and calculation package in R
Principle

Calculation

Index

Abb.

Bioacoustic Index

Bio

Biophony Level

19

soundecology

Amplitude Index

M()

Median of Amplitude Envelope

20

seewave

Temporal Entropy

HtEnvelope Complexity

21

seewave

Acoustic RichnessAR20

seewave

Spectral Entropy

Hf21

seewave

Acoustic Entropy IndexH

21

seewave

Acoustic Evenness Index

AEI

23

soundecology

24soundecology

25

seewave

Acoustic Complexity
Index
Number of Peaks

ACI
NP

Envelope Complexity andIntensity
Spectrum Complexity
Envelope and Spectrum
Complexity
Envelope and Spectrum
Complexity
Spectrogram Complexity
Spectrogram Complexity

Reference

Package in R

Ratio of Anthrophony toNormalized DifferenceSoundscape Index

NDSI

Biophony26

soundecology

Shannon's Index

H'

Spectrum Complexity

26

seewave

Beta sound indices, their origin and calculation package in R
Index

Abb.

Spectral Dissimilarity

Df

Wave Dissimilarity-

Itakura-Saito Distance-Temporal Dissimilarity

Dt

Acoustic Dissimilarity
Index
Kolmogorov-Smirnov
Distance
Kullback Leibler

D
KS
KL

Principle
Spectrum Dissimilarity
Absolute Difference Between
Frequency Bins
Difference Between two
Probability Mass Functions
Envelope Dissimilarity
Envelope and Spectrum
Dissimilarity
Spectrum Dissimilarity
Spectrum Dissimilarity

ReferenceCalculation
Package in R

21

seewave21

seewave16

seewave21

seewave21

seewave28

seewave28

seewave

847

Distance
I-Mutual Information
Relative Frequency
Dissimilarity
Correlation-based
Dissimilarity

RV Dissimilarity
Cumulative Spectral
Dissimilarity

RV
Dcf

Entropy of Each Frequency
Spectrum and the Joint Entropy
Dissimilarity between
Frequency Bins
Pearson’s Correlation between
Frequency Bins
Spectrum Dissimilarity
Spectrum Dissimilarity

16

seewave16

seewave

16

Stats

28

FactoMineR

29

seewave

However, R Studio does not cover all possible analysis tools. Six indices were identified whichcannot currently be calculated in R (Table 2). Towsey et al. (17) used the ART1 unsupervised iterative
learning algorithm (18) to automatically classify number and duration of sound clusters. This approach
is more complex than what the scope of the project can handle. Rather, the program Kaleidoscope(Wildlife Acoustics) contains an automated supervised sound identification and classification
algorithm, which is more realistic for implementation within the project scope and it can handle large
datasets.
Table 2 – Sound indices not included in R, but included in Kaleidoscope
Index
Sound Pressure Level
Parameters
Mid-Band Activity
Entropy of Spectral
MaximaEntropy of Spectral
Variance

Abb.
L
Hm
Hv

Principle
Ratio of Sound Pressure
Relative to a Reference Value
Fraction of Spectrum Above an
Amplitude Threshold
Spectrum Composition
Spectrum Composition

Reference-

Platform

30

-

17

-

17, 1817, 18

Number of Clusters
Spectral Diversity

Calculation

Luscinia GPL
v2 / ART 1
Luscinia GPL
v2 / ART 1
Luscinia GPL

17, 18v2 / ART 1 /Kaleidoscope

Spectral Persistence-

Duration of Repeated Clusters

17, 18ART 1

Five indices specifically designed for classification of bird sounds were not included given that theaim of SALVE is not to characterize only avifaunal sounds, but to analyze the full frequency range or
ranges in combination with biophonic and anthrophonic frequency spectrums ( Table 3).
Kaleidoscope will automatically identify which bird species are present in recordings using a
supervised classification method, which is more accurate than a cumulative bioacoustics index in thisregard. The NDSI will also quantify the proportion of biophonic sounds in sound samples.
Table 3 – Sound indices focusing only on biophonic sources, not calculated in R
Index

848

Abb.

Principle

ReferenceCalculation

Platform
Ratio of Biophony to
Anthrophony
Biophony
Biophony PeakMedian of Amplitude
Envelope
Relative Avian
Abundanceρ

Ratio of Biophony to
Anthrophony-

Biophony Level

bio-Peak

Biophony Level

MAEMedian and Amplitude
Envelope

31

-

23

-

32

-

20

-

19

-

Area Under Spectrum in-

Relation to an Amplitude
Threshold

Past studies on the urban sound environment, traffic noise and noise mapping have used a wide
range of decibel-based measurements (10, 12, 13). To be able to compare past studies with SALVE andprovide insights that the WAM approach could yield in sound environment studies, nine of thesedecibel measures are included in WAM. These measures are calculated automatically by the NTi
sound measurement device employed in the SALVE project, by the Kaleidoscope program forrecordings from the AAD devices, or are computed within Excel as combinations of decibel
measurements from the NTi recording log or Kaleidoscope (Table 4).
Table 4 – Decibel-based indices for inclusion in the WAM approach
Index
Equivalent Continuous
Sound Level
Day-night Level
Day-Evening-Night
Level

Abb.
Leq
LDN
Lden

Traffic Noise Index

TNI

Noise Pollution Index

LNP

Equivalent Sound
Pressure Level

LAeq

Level L10

L10

Level L50

L50

Level L90

L90

Principle
Constant Average Decibel
Level
24H Lp average with 10dB
Added to Evening Hours
24H Lp Average with 5dB
Added to Evening Hours
An Algorithm Composed of
L10 and L90 Levels
An LAeq Variation Accounting
for Short-Term Variability
Total Sound Energy Received at
a Location
Noise Level Exceeded 10% of
the Time
Noise Level Exceeded 50% of
the Time
Noise Level Exceeded 90% of
the Time

ReferenceCalculation
Platform

10

NTi

10

Excel

10

Excel

10

Excel

10

Excel

10

NTi

11

NTi

11

NTi

11

NTi

849

The literature review concludes that a combination of the 23 alpha and beta indices that can be
automated in R Studio, plus the one cluster analysis automated in Kaleidoscope, plus nine
decibel-based measures will result in a WAM of 33 indices that cover a broad spectrum of soundindices. The Acoustic Diversity Index (22) has been excluded from WAM since it was found to containan error, thus Shannon’s Index will represent acoustic diversity.

4. DISCUSSION
Given the breadth of alpha and beta indices described in Sueur (16), one could argue that theutilization of variations of the single decibel (dB) measure to understand the urban acoustic
environment would limit the total understanding of the acoustic environment compared to an approachthat both dB and frequency (Hz) measures might be able to provide. When considering sound as dB
alone, the traditional understanding has been in terms of sound pollution and decibel level durations
that lead to human harm. However, studies using the psychoacoustic approach described in Farina (1)and Kang (10) and solidified in ISO 12913-2 (4) have indicated that the total acoustic environment,
rather than dB levels alone, can play a role in human soundscape perception in different acoustic
environments. Thus, the expanded WAM approach will quantify sound as both dB and Hz, overspatial-temporal extents.
The use of alpha and beta indices, as well as dB measures, will allow a wide range of metrics to beevaluated against health outcomes and spatial structure, thereby offering the greatest chance for the
explanation of human health outcomes or soundscape preference. Alpha metrics compare the soundsample within itself, for loudness, frequency dominance, evenness, directionality of biophonic oranthrophonic sounds, frequency complexity, diversity and dissimilarity. The beta measures compare
sound recordings between each other, allowing the sound spectrum similarity or dissimilarity to beclustered and potentially for clusters to be defined by an array of sound sources. The beta indices will
be useful to define different urban sonotopes, which may have limited dB differences, but throughcluster analysis can be shown to be composed of different sound sources. Kaleidoscope will help
isolate and identify individual sound sources from recordings and in clusters. Analysis with WAM
will enable a broad array of comparisons between urban structure and resulting acoustic environments .
The addition of Hz indices may clarify the differences between the spread of sound pressure levels(SPL) as compared to frequencies in the urban acoustic environment.
Two composite scripts containing all 11 alpha and all 12 beta indices will be developed in the R
language to automate the calculation. Based on the initial development of the alpha script, thecalculation for the 2920 DAP observations (730x4) is projected to take about 18 hours, whereas thecalculation for the 493,555 AAD observations will likely require more than 120 days and an array of 4
PCs running parallel temporal batches will be required. The beta indices, which compare allobservations against each other, will be calculated using a super computer at TU Dortmund.

REFERENCES
1. Farina, Almo. Soundscape Ecology: Principles, Patterns, Methods and Applications. Urbino, Italien:
Springer; 2014.
2. Orban, E., Sutcliffe, R., Dragano, N., Jöckel, K. H., & Moebus, S.. Residential surrounding greenness,
self-rated health and interrelations with aspects of neighborhood environment and social relations.
Journal of Urban Health. 2017; 94(2), 158-169.
3. Schmermund, A., Möhlenkamp, S., Stang, A., et al.. Assessment of clinically silent atherosclerotic
disease and established and novel risk factors for predicting myocardial infarction and cardiac death inhealthy middle-aged subjects: Rationale and design of the Heinz Nixdorf RECALL Study. American
Heart Journal.2002 144, 212-218.
4. ISO 12913-2. Acoustics – soundscape – part 2: Technical Supplement. 2018
5. Pijanowski, B. C., Villanueva-Rivera, L. J., Dumyahn, S. L., Farina, A., Krause, B. L., Napoletano, B. M., ...
& Pieretti, N. Soundscape ecology: the science of sound in the landscape. BioScience. 2011; 61(3), 203-216.
6. Forman, Richard TT and Godron, Michael. Landscape Ecology. NY: Wiley and Sons; 1986.

7. Baudry, J., Zonneveld, I. S., & Forman, R. T.. Changing landscapes: an ecological perspective. Springer;
1990.
8. Villanueva-Rivera, L. J., Pijanowski, B. C., & Villanueva-Rivera, M. L. J.. Package ‘soundecology’.
2018

850

9. Ihaka, R.R. and Gentleman, R.. R: A language for data analysis and graphics. J. Comput Graph Stat.
1996; 5:299-314.
10. Kang, J.. Urban sound environment. CRC Press. 2006
11. Wei, Weigang; van Renterghem, Timothy; Coensel, Bert de; Botteldooren, Dick. Dynamic noise
mapping: A map-based interpolation between noise measurements with high temporal resolution. In
Applied Acoustics. 2016; 101, pp. 127–140. DOI: 10.1016/j.apacoust.2015.08.005.
12. Margaritis, Efstathios; Kang, Jian; Filipan, Karlo; Botteldooren, Dick. The influence of vegetation andsurrounding traffic noise parameters on the sound environment of urban parks. In Applied Geography.
2018; 94, pp. 199–212. DOI: 10.1016/j.apgeog.2018.02.017.
13. Rey Gozalo, G.; Trujillo Carmona, J.; Barrigón Morillas, J. M.; Vílchez-Gómez, R.; Gómez Escobar, V..
Relationship between objective acoustic indices and subjective assessments for the quality ofsoundscapes. In Applied Acoustics. 2015; 97, pp. 1–10. DOI: 10.1016/j.apacoust.2015.03.020.
14. Ganann, R., Ciliska, D., & Thomas, H.. Expediting systematic reviews: methods and implications ofrapid reviews. Implementation Science. 2010; 5(1), 56.
15. Sueur, Jerome; Farina, Almo; Gasc, Amandine; Pieretti, Nadia; Pavoine, Sandrine. Acoustic Indices for
Biodiversity Assessment and Landscape Investigation. In Acta Acustica united with Acustica. 2014; 100(4), pp. 772–781. DOI: 10.3813/AAA.918757.
16.Sueur, J., Simonis, C., Brown, E. C., Desjonqueres, C., Gasc, A., Marchal, J., ... & Witthoft, C. G..
Sound Analysis and Synthesis with R. Springer; 2018
17. Towsey, Roe, Paul; Wimmer, Michael; Williamson, Ian Jason. The use of acoustic indices to determine
avian species richness in audio-recordings of the environment. In Ecological Informatics. 2014; 21,
pp. 110–119. DOI: 10.1016/j.ecoinf.2013.11.007.
18. Carpenter, G. A., & Grossberg, S.. A self-organizing neural network for supervised learning, recognition,and prediction. Cognitive modeling, 2002; 289-314.
19. N. T. Boelman, G. P. Asner, P. J. Hart, R. E. Martin: Multitrophic invasion resistance in Hawaii:bioacoustics, field surveys, and airborne remote sensins. Ecological Applications. 2007; 17: 2137–2144.
20. M. Depraetere, S. Pavoine, F. Jiguet, A. Gasc, S. Duvail, J. Sueur: Monitoring animal diversity using
acoustic indices: implementation in a temperate woodland. Ecological Indicators. 2012; 13: 46–54.
21. J. Sueur, S. Pavoine, O. Hamerlynck, S. Duvail: Rapid acoustic survey for biodiversity appraisal. PLoS
ONE. 2008; 3: e4065.
22. B. Pekin, J. Jung, L. Villanueva-Rivera, B. Pijanowski, J. Ahumada: Modeling acoustic diversity using
soundscape recordings and LIDAR-derived metrics of vertical forest structure in a neotropical rainforest.
Landscape Ecology. 2012; 27: 1513–1522.
23. W. Joo, S. H. Gage, E. P. Kasten: Analysis and interpretation of variability in soundscapes along anurban rural gradient. Landscape and Urban Planning. 2011; 103: 259–276.
24. N. Pieretti, A. Farina, D. Morri: A new methodology to infer the singing activity of an avian
community: the Acoustic Complexity Index (ACI). Ecological Indicators. 2011; 11: 868–873.
25. A. Gasc, J. Sueur, S. Pavoine, R. Pellens, P. Grandcolas: Biodiversity sampling using a global acoustic
approach: contrasting sites with microendemics in New Caledonia. PLoS ONE. 2013; 8: e65311.
26. E. P. Kasten, S. H. Gage, J. Fox, W. Joo: The remote environmental assessment laboratory’s acoustic
library: an archive for studying soundscape ecology. Ecological Informatics. 2012; 12: 50–67.
27. L. Villanueva-Rivera, B. Pijanowski, J. Doucette, B. Pekin: A primer of acoustic analysis for landscapeecologists. Landscape Ecology. 2011; 26: 1233–1246.
28. A. Gasc, J. Sueur, F. Jiguet, V. Devictor, P. Grandcolas, C. Burrow, M. Depraetere, S. Pavoine:
Assessing biodiversity with sound: Do acoustic diversity indices reflect phylogenetic and functional
diversities of bird communities? Ecological Indicators. 2013; 25: 279–287.
29. L. Lellouch, S. Pavoine, F. Jiguet, H. Glotin, J. Sueur: Monitoring temporal change of bird communities
with dissimilarity acoustic indices. Methods in Ecology and Evolution. 2014: doi: 10.1111/2041–
210X.12178.
30. M. Rychtáriková, G. Vermeir: Soundscape categorization on the basis of objective acousticalparameters. Applied Acoustics. 2013; 74: 240–247.
31. J. Qi, S. H. Gage, W. Joo, B. Napoletano, S. Biswas: Soundscape characteristics of an environment. A
new ecological indicator of ecosystem health. – In: Wetland and water resource modeling andassessment. J. Wei (ed.). CRC Press, Boca Raton: 2008; 201–211.
32. B. Krause, S. H. Gage, W. Joo: Measuring and interpreting the temporal variability in the soundscape atfour places in Sequoia National Park. Landscape Ecology. 2011; 26: 1247–1256.

851