Measurement-Based Automatic Parameterization of a Virtual Acoustic Room Model

Modernien auralisaatiotekniikoiden ansiosta kuulokkeilla voidaan tuottaa kuuntelukokemus, joka muistuttaa useimpien äänitteiden tuotannossa oletettua kaiutinkuuntelua. Huoneakustinen mallinnus on tärkeä osa toimivaa auralisaatiojärjestelmää. Huonemallinnuksen parametrien määrittäminen vaatii kuitenkin ammattitaitoa ja aikaa. Tässä työssä kehitetään järjestelmä parametrien automaattiseksi määrittämiseksi huoneakustisten mittausten perusteella. Parametrisaatio perustuu mikrofoniryhmällä mitattuihin huoneen impulssivasteisiin ja voidaan jakaa kahteen osaan: suoran äänen ja aikaisten heijastusten analyysiin sekä jälkikaiunnan analyysiin. Suorat äänet erotellaan impulssivasteista erilaisia signaalinkäsittelytekniikoita käyttäen ja niitä hyödynnetään heijastuksia etsivässä algoritmissa. Äänilähteet ja heijastuksia vastaavat kuvalähteet paikannetaan saapumisaikaeroon perustuvalla paikannusmenetelmällä ja taajuusriippuvat etenemistien vaikutukset arvioidaan kuvalähdemallissa käyttöä varten. Auralisaation jälkikaiunta on toteutettu takaisinkytkevällä viiveverkostomallilla. Sen parametrisointi vaatii taajuusriippuvan jälkikaiunta-ajan ja jälkikaiunnan taajuusvasteen määrittämistä. Normalisoitua kaikutiheyttä käytetään jälkikaiunnan alkamisajan löytämiseen mittauksista ja simuloidun jälkikaiunnan alkamisajan asettamiseen. Jälkikaiunta-aikojen määrittämisessä hyödynnetään energy decay relief -metodia. Kuuntelukokeiden perusteella automaattinen parametrisaatiojärjestelmä tuottaa parempia tuloksia kuin parametrien asettaminen manuaalisesti huoneen summittaisten geometriatietojen pohjalta. Järjestelmässä on ongelmia erityisesti jälkikaiunnan ekvalisoinnissa, mutta käytettyihin suhteellisen yksinkertaisiin tekniikoihin nähden järjestelmä toimii hyvin.Modern auralization techniques enable making the headphone listening experience similar to the experience of listening with loudspeakers, which is the reproduction method most content is made to be listened with. Room acoustic modeling is an essential part of a plausible auralization system. Specifying the parameters for room modeling requires expertise and time. In this thesis, a system is developed for automatic analysis of the parameters from room acoustic measurements. The parameterization is based on room impulse responses measured with a microphone array and can be divided into two parts: the analysis of the direct sound and early reflections, and the analysis of the late reverberation. The direct sounds are separated from the impulse responses using various signal processing techniques and used in the matching pursuit algorithm to find the reflections in the impulse responses. The sound sources and their reflection images are localized using time difference of arrival -based localization and frequency-dependent propagation path effects are estimated for use in an image source model. The late reverberation of the auralization is implemented using a feedback delay network. Its parameterization requires the analysis of the frequency-dependent reverberation time and frequency response of the late reverberation. Normalized echo density is used to determine the beginning of the late reverberation in the measurements and to set the starting point of the modeled late field. The reverberation times are analyzed using the energy decay relief. A formal listening test shows that the automatic parameterization system outperforms parameters set manually based on approximate geometrical data. Problems remain especially in the precision of the late reverberation equalization but the system works well considering the relative simplicity of the processing methods used

Study area.

<p>Map of modern sites with estimated areas of historical sampling superimposed (see Appendix C 2.1 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0093092#pone.0093092.s010" target="_blank">File S2</a> for more information). GIS data from WRI <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0093092#pone.0093092-World1" target="_blank">[41]</a>.</p

Data Paper. Data Paper

<h2>File List</h2><div> <p><a href="KMD13_occur.csv">KMD13_occur.csv</a> (MD5: 817da6ade01a91e557f2211b82831361)</p> <p><a href="KMD13_sources.csv">KMD13_sources.csv</a> (MD5: a8be2715bc59257dcf43c610738db988)</p> <p><a href="KMD13_traits.csv">KMD13_traits.csv</a> (MD5: 4a9dceb8a8bb8299a1f61d073bfb2b56)</p> <p><a href="KMD13_Lformat.csv">KMD13_Lformat.csv</a> (MD5: 1ddaa3b060053b9584a9eb90f6a36573)</p> <p><a href="Site_Chars.csv">Site_Chars.csv</a> (MD5: 3d75cda6f57e4af118b372a0885909d4)</p> </div><h2>Description</h2><div> <p>Kenya is a world leader in conservation and host to one of the most diverse array of mammals on the planet. As a focus of scientific attention, it is important to be able to assess not only the current state of Kenya's mammal communities, but also how they have changed over anthropogenic timescales. Comprehensive lists of mammal species from known areas are essential for this goal, and these also provide comparative baselines for assessing changes in mammalian diversity in the future and in the fossil record. Though there is considerable literature available for mammals inhabiting Kenyan protected areas (National Parks and Reserves), species compilation projects vary greatly in scope, completeness, agreement, and accuracy. We combine the information in these databases for Kenya and supplement them with the most up-to-date knowledge available up to November 2013. Comprehensive historical species lists were compiled from specimen lists collected during 1888–1950 in ecosystems that today correspond to 13 different protected areas.  We also provide analogous modern species lists based on data collected during 1950–2012. The data sets include both large and small mammals. A master list of a total of 413 species provides ecological information including body mass, diet, feeding and shelter habitat, and activity time. Historical data are based on museum specimens and sighting records, and modern data are based on museum data as well as literature, books, field guides, written accounts, photos, and videos. We used this compilation for an analysis comparing the two data sets (excluding volant and domestic species) for six protected areas with the most complete historical records and have shown in a separate publication that species richness is preserved, but beta diversity, based on pairwise comparisons of sites in this database, is being lost over the entire study area. </p> <p> <i>Key words</i>: <i>biodiversity; community ecology; East Africa; ecology; large mammals; mammals; protected areas; small mammals; species lists. </i> </p> </div

A Century of Change in Kenya's Mammal Communities: Increased Richness and Decreased Uniqueness in Six Protected Areas

<div><p>The potential for large-scale biodiversity losses as a result of climate change and human impact presents major challenges for ecology and conservation science. Governments around the world have established national parks and wildlife reserves to help protect biodiversity, but there are few studies on the long-term consequences of this strategy. We use Kenya as a case study to investigate species richness and other attributes of mammal communities in 6 protected areas over the past century. Museum records from African expeditions that comprehensively sampled mammals from these same areas in the early 1900's provide a baseline for evaluating changes in species richness and community structure over time. We compare species lists assembled from archived specimens (1896–1950) to those of corresponding modern protected areas (1950–2013). Species richness in Kenya was stable or increased at 5 out of 6 sites from historical to modern times. Beta-diversity, in contrast, decreased across all sites. Potential biases such as variable historical vs. modern collection effort and detection of small-bodied, rare, and low-visibility species do not account for the observed results. We attribute the pattern of decreased beta diversity primarily to increased site occupancy by common species across all body size classes. Despite a decrease in land area available to wildlife, our data do not show the extinctions predicted by species-area relationships. Moreover, the results indicate that species-area curves based solely on protected areas could underestimate diversity because they do not account for mammal species whose ranges extend beyond protected area boundaries. We conclude that the 6 protected areas have been effective in preserving species richness in spite of continuing conversion of wild grasslands to cropland, but the overall decrease in beta diversity indicates a decline in the uniqueness of mammal communities that historically characterized Kenya's varied landscape.</p></div

Richness and β-Diversity.

<p>(A) Comparison of species richness by site. Color code: yellow = 1896–1950, green = 1950–2013. A paired t-test indicates this is a significant increase in richness when considering all sites (t = 2.215, p = 0.039). (B) Degree of similarity between each pair of sites in the historical and modern records, using the Sorensen Index. Size of filled circles indicates degree of similarity for each pair (See Table S3 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0093092#pone.0093092.s009" target="_blank">File S1</a> for exact values). Comparisons of circles in different time periods show an increase in similarity for each site pair, thus a decrease in beta-diversity (Table S4 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0093092#pone.0093092.s009" target="_blank">File S1</a>). Wilcoxon signed-rank test: p<0.0001.</p

Effects of occupancy changes on β-Diversity.

<p>Net effect of occupancy change between historical and modern time intervals. The peaks represent species (A) at one site that disappeared, (B) appearing at one site and (C) originally at 2–5 sites that increased their occupancies. Peaks A and B show predicted rare mammal turnover <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0093092#pone.0093092-Hanski1" target="_blank">[43]</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0093092#pone.0093092-Magurran1" target="_blank">[44]</a>, which effectively cancel each other out. Peak C includes species across all body size classes and drives the pattern of increasing similarity from historical to modern times.</p

Site information and gamma-diversity counts.

<p>Modern areas calculated based on the size of modern protected areas. Historical areas were calculated from geo-referenced historical maps of the Roosevelt expedition. M = modern; H = historical; Shared H&M = number of species that occurred in both time periods; Total H+M  =  total number of species recorded in any time period without overlaps.</p

Similarity increase among sites.

<p>Non-metric multidimensional scaling (NMDS) using the Jaccard Coefficient for species lists from 6 sites (Table S3 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0093092#pone.0093092.s009" target="_blank">File S1</a>), showing that 5 sites move closer together from historical (dashed line, open points) to the modern (solid line, filled points), and overall spread (polygons) decreases (area of historical hull = 0.629; modern hull = 0.155; Stress (av. of 4 runs) = 0.0746). Key: KK = Kakamega, MM = Maasai Mara, NV = Naivasha, NR = Nairobi, SB = Samburu, TV = Tsavo.</p

Kenya's wildlife policies.

<p>A timeline summarizing Kenya's policy on natural resources and wildlife over the past 120 years <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0093092#pone.0093092-Waithaka1" target="_blank">[11]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0093092#pone.0093092-The1" target="_blank">[18]</a>.</p

Miller et al 2014 Paleobiology SOM and Appendices

Supplemental Online Materials and Appendices for "Ecological fidelity of functional traits based on species presence-absence in a modern mammalian bone assemblage (Amboseli, Kenya).