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

SculpsAdapChar14

This is the .csv file that contains the data

Knope_Scales_sculpin_adapt

This file is the R code that runs OUCH (Butler and King, Am Nat 2004) on the sculpin body size and scale data. Each line of code is annotated to describe its specific function

The time-tree showing families analyzed in this study and their times of origin.

<p>The tree was pruned from an extensive species-level time tree provided to us by Richard Betancur-R, using data from [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0188888#pone.0188888.ref013" target="_blank">13</a>]. Branches along the tree indicate families. Color coding shows clades across the tree, and matches <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0188888#pone.0188888.g003" target="_blank">Fig 3</a>.</p

Key to the peak (in red) and off-peak (black) regions of the morphospace landscape analyzed by <i>convevol</i>, and reported in Table 3.

<p><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0188888#pone.0188888.g003" target="_blank">Fig 3</a> shows the details of the morphospace.</p

In this zebrafish <i>mef2ca</i> mutant a bridge of bone (*) forms between the OP anterior-ventral bone and a branchiostegal ray (BSR).

<p>Early larva stage–mutants die soon after this time (examples from [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0188888#pone.0188888.ref017" target="_blank">17</a>]; see also [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0188888#pone.0188888.ref018" target="_blank">18</a>]).</p

<i>Convevol</i> frequency-based measurement yields evidence for convergent evolution on the principal landscape occupancy peak (termed peak 1 in Table 3) of PC1 by PC2 phylomorphospace.

<p>The large points show terminal taxa (families) corresponding to points of the landscape plot shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0188888#pone.0188888.g003" target="_blank">Fig 3</a>. The smaller points show inferred positions of ancestors. Note the evolutionary trajectories, many of them quite long, that crisscross the landscape in a variety of directions. The ellipse (lower panel shows a zoom in) indicates the occupancy peak of interest, and the red arrows show independent entries (convergences) into this region. See text and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0188888#pone.0188888.t003" target="_blank">Table 3</a> for further explanation.</p

Principal component-based morphospace occupancy.

<p>PCA reveals that most of the bone shapes densely occupy a single large region of the space, here constructed from the two leading eigenvectors, PC1 and PC2, derived from landmark analysis. The percentages within the parentheses give variances explained. The accompanying overlay configurations show shape changes associated with the PC axes. PC1 captures details of the outlines: forked OPs toward high positive PC1 values and smooth configurations, often with prominent dorsal regions (upper in the figure) toward negative PC1 values. In contrast PC2 separates bones that are thin along the anterior-posterior axis (positive PC2 or much broader along this axis (negative PC2. The data points of the scatter plot show single exemplar species from each of the families across the phylogeny in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0188888#pone.0188888.g004" target="_blank">Fig 4</a>, with matching colors between the points here and the branches of the tree. The lines included with the scatterplot show nonparametric density contours. Heat map colored contours are according to occupancy density, with warm colors indicating density peaks. Morphospaces using other PCs (e.g. PC3 by PC4) also reveal dense occupancies.</p

Frequency (C5) and similarity (C1-C4) based measures and probabilities (p) examining convergences within the indicated focal regions of the morphospace.

<p><i>Convevol</i> analyses.</p

Tests of evolutionary models using the GEIGER platform, based on individual Principal Components.

<p>Tests of evolutionary models using the GEIGER platform, based on individual Principal Components.</p