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

Supplementary Figure 6

Comparing statistical dynamics for different ecospace framework structures: varying number of characters, (A) 5 characters, (B) 15 characters, and (C) 25 characters. Each framework had mixed character types, in identical proportions (40% binary, 20% three-state factor, 20% five-state factor, and, 20% five-state ordered numeric character types). 5 "seed" species were chosen at random to begin each simulation. Other simulation details and graphical interpretation are the same as is Figure 2. Trends in total variance were excluded because the inclusion of factors prevented their calculation. The dynamics are generally similar, although larger frameworks allow modestly more powerful model selection using classification-tree methods (83%, 85%, and 86% of training models, respectively, classified correctly using classification-tree methods). See Supplementary Appendix 2 for additional details

Life-habit/functional-trait codings for the Kope and Waynesville Formation species pool

KWTraits.csv is a comma-separated value (.csv) format file listing the aggregate species pool for the Kope and Waynesville Formation used in empirical analyses. (The file is also included as a data file within the 'ecospace' R package.) The first three columns list taxonomic information. The remaining columns list ecospace character states (functional traits). See supplementary appendix A and Novack-Gottshall (2007) for information on characters and states. See text for explanation of how multistate characters were rescaled

Supplementary Appendices 1-4 for manuscript

Appendix 1 gives an example of how life-habit character states were inferred and coded. Appendix 2 describes technical details on classification tree methods and confusion matrices. Appendix 3-4 give further details for the other Supplementary data files on Data Dryad

Three-model model-selection support data files for Kope and Waynesville Formation samples, stratigraphic section, member, and formation aggregates

File is in comma-separated value (.csv) format. The first five columns describe the Paleobiology Database collection identification number, scale (hand sample, stratigraphic section, etc.) of the sample, and stratigraphic/section names. Columns 6–14 list sample size (S, species richness) and values for eight disparity statistics (with NA designating when a statistic could not be calculated, because there were fewer than four unique life habits in the sample); see text for descriptions and abbreviations of statistics. The last column identifies which model has the best support among those candidates considered. The remaining columns list the classification-tree support each sample has for each candidate model considered. emp3-modelfits.csv lists model support for the tree trained on 50%, 90%, and 100% training data

Supplementary Figures from Hierarchical complexity and the size limits of life

Supplementary Figures S1 - S