identifying acoustical coupling by measurements and prediction models for st peter s basilica in rome st peter s basilica is one of the largest buildings in the world having a huge volume resulting from the addition of different parts consequently sound propagation cannot be interpreted using a conventional approach and requires experimental measures to be compared with statistical acoustics and geometrical predictions in order to explain the interplay between shape materials and sound waves better in previous research one of the most evident effects the surprisingly low reverberation time was believed to result from acoustical coupling phenomena taking advantage of more refined measuring techniques available today an acoustic survey was carried out and the results were analyzed using different methods including bayesian parameter estimation of multiple slope decays and directional energy plots which showed that coupling effects actually take place even though measured reverberation times were longer than those given in previous studies in addition experimental results were compared with geometrical and statistical acoustic models of the basilica which showed that careful selection of input data and in statistical models the inclusion of phenomena such as direct sound radiation and non diffuse energy transfer allow obtaining accurate results finally both models demonstrated that reduced reverberation depends more on increased absorption of decorated surfaces than on coupling effects

coupling detrended fluctuation analysis for analyzing coupled nonstationary signals when many variables are coupled to each other a single case study could not give us thorough and precise information when these time series are stationary different methods of random matrix analysis and complex networks can be used but in nonstationary cases the multifractal detrended cross correlation analysis mf dxa method was introduced for just two coupled time series in this article we have extended the mf dxa to the method of coupling detrended fluctuation analysis cdfa for the case when more than two series are correlated to each other here we have calculated the multifractal properties of the coupled time series and by comparing cdfa results of the original series with those of the shuffled and surrogate series we can estimate the source of multifractality and the extent to which our series are coupled to each other we illustrate the method by selected examples from air pollution and foreign exchange rates

multifractal detrended cross correlation analysis for two nonstationary signals we propose a method called multifractal detrended cross correlation analysis to investigate the multifractal behaviors in the power law cross correlations between two time series or higher dimensional quantities recorded simultaneously which can be applied to diverse complex systems such as turbulence finance ecology physiology geophysics and so on the method is validated with cross correlated one and two dimensional binomial measures and multifractal random walks as an example we illustrate the method by analyzing two financial time series

evaluation of decay times in coupled spaces bayesian decay model selection this paper applies bayesian probability theory to determination of the decay times in coupled spaces a previous paper n xiang and p m goggans j acoust soc am 110 1415 1424 2001 discussed determination of the decay times in coupled spaces from schroeder s decay functions using bayesian parameter estimation to this end the previous paper described the extension of an existing decay model n xiang j acoust soc am 98 2112 2121 1995 to incorporate one or more decay modes for use with bayesian inference bayesian decay time estimation will obtain reasonable results only when it employs an appropriate decay model with the correct number of decay modes however in architectural acoustics practice the number of decay modes may not be known when evaluating schroeder s decay functions the present paper continues the endeavor of the previous paper to apply bayesian probability inference for comparison and selection of an appropriate decay model based upon measured data following a summar

multifractal properties of price change and volume change of stock market indices abstract we study auto correlations and cross correlations of daily price changes and daily volume changes of thirteen global stock market indices using multifractal detrended fluctuation analysis mf dfa and multifractal detrended cross correlation analysis mf dxa we find rather distinct multifractal behavior of price and volume changes our results indicate that the time series of price changes are more complex than those of volume changes and that large fluctuations dominate multifractal behavior of price changes while small fluctuations dominate multifractal behavior of volume changes we also find that there is an absence of correlations in price changes there are anti persistent long term correlations in volume changes and there are anti persistent long term cross correlations between price and volume changes shuffling the series reveals that multifractality of both price changes and volume changes arises from a broad probability density function

the scaling properties of stock markets based on modified multiscale multifractal detrended fluctuation analysis abstract here we propose the new method dh mma based on multiscale multifractal detrended fluctuation analysis mma to investigate the scaling properties in stock markets it is demonstrated that our approach can provide a more stable and faithful description of the scaling properties in comprehensive range rather than fixing the window length and slide length it allows the assessment of more universal and subtle scaling characteristics we illustrate dh mma by selecting power law artificial data sets and six stock markets from us and china the us stocks exhibit very strong multifractality for positive values of q however the chinese stocks show stronger multifractality for negative q than positive q in general the us stock markets show similar behaviors but chinese stock markets display distinguishing characteristics

chaos based nonlinear analysis to study cardiovascular responses to changes in posture abstract cardiovascular responses to changes in posture have been the focus of numerous investigations in the past analysis of ecg and arterial blood pressure abp data for different postures obtained from physionet was done from a new perspective of analyzing nonlinear time series having multifractal features multifractal detrended fluctuation analysis mfdfa and also the cross correlation between ecg and bp with another technique multifractal detrended cross correlation analysis mfdxa reveals ecg and abp time series of different subjects to be multifractal in nature with different degree of complexity different degree of auto and cross correlation for both ecg and abp signals is also observed for all subjects further significant information obtained from the cross correlation parameters obtained from mfdxa analysis is very important and relevant particularly for understanding the dynamics of orthostatic stress

sound propagation and energy relations in churches the results of an acoustic survey carried out in a group of italian churches differing in style typology and location were used in order to study how the acoustic energy varies inside this kind of space the effect of different architectural elements on sound propagation was investigated by means of three dimensional impulse responses measured using a b format microphone with sweep signals side chapels columns and trussed roofs appeared to scatter the reflections so that the purely diffuse exponential sound decay begins after a time interval which grows with the source receiver distance and with the complexity of the church the results of the measurements were then compared with predictions given by existing theoretical models to check their accuracy in particular a model previously proposed by the authors for a specific type of romanesque churches was further refined taking into account the new findings and making some simplifications its application to the wider sample of churches under analysis showed that strength clarity and center time can be predicted with reasonable accuracy

multifractal detrended fluctuation analysis of nonstationary time series abstract we develop a method for the multifractal characterization of nonstationary time series which is based on a generalization of the detrended fluctuation analysis dfa we relate our multifractal dfa method to the standard partition function based multifractal formalism and prove that both approaches are equivalent for stationary signals with compact support by analyzing several examples we show that the new method can reliably determine the multifractal scaling behavior of time series by comparing the multifractal dfa results for original series with those for shuffled series we can distinguish multifractality due to long range correlations from multifractality due to a broad probability density function we also compare our results with the wavelet transform modulus maxima method and show that the results are equivalent

autocorrelation and cross correlation in time series of homicide and attempted homicide abstract we propose in this paper to establish the relationship between homicides and attempted homicides by a non stationary time series analysis this analysis will be carried out by detrended fluctuation analysis dfa detrended cross correlation analysis dcca and dcca cross correlation coefficient d c c a n through this analysis we can identify a positive cross correlation between homicides and attempted homicides at the same time looked at from the point of view of autocorrelation dfa this analysis can be more informative depending on time scale for short scale days we cannot identify auto correlations on the scale of weeks dfa presents anti persistent behavior and for long time scales n 90 days dfa presents a persistent behavior finally the application of this new type of statistical analysis proved to be efficient and in this sense this paper can contribute to a more accurate descriptive statistics of crime

multifractal fourier detrended cross correlation analysis of traffic signals abstract multifractal detrended cross correlation analysis mf dxa has been developed to detect the long range power law cross correlation of considered signals in the presence of non stationarity however crossovers arising from extrinsic periodic trends make the scaling behavior difficult to analyze we introduce a fourier filtering method to eliminate the trend effects and systematically investigate the multifractal cross correlation of simulated and real traffic signals the crossover locations are found approximately corresponding to the periods of underlying trend traffic velocity on one road and flows on adjacent roads show strong cross correlation they also present weak multifractality after periodic trends are removed the traffic velocity and flow are cross correlated in opposite directions which is accordant to their actual evolution

multifractal detrended cross correlation analysis of sunspot numbers and river flow fluctuations abstract we use the detrended cross correlation analysis dcca to investigate the influence of sun activity represented by sunspot numbers on one of the climate indicators specifically rivers represented by river flow fluctuation for daugava holston nolichucky and french broad rivers the multifractal detrended cross correlation analysis mf dxa shows that there exist some crossovers in the cross correlation fluctuation function versus time scale of the river flow and sunspot series one of these crossovers corresponds to the well known cycle of solar activity demonstrating a universal property of the mentioned rivers the scaling exponent given by dcca for original series at intermediate time scale 12 24 s 130 months is 1 17 0 04 which is almost similar for all underlying rivers at 1 confidence interval showing the second universal behavior of river runoffs to remove the sinusoidal trends embedded in data sets we apply the singular value decomposition svd method our results show that there exists a long range cross correlation between the sunspot numbers and the underlying streamflow records the magnitude of the scaling exponent and the corresponding cross correlation exponent are 0 76 0 85 and 0 30 0 48 respectively different values for scaling and cross correlation exponents may be related to local and external factors such as topography drainage network morphology human activity and so on multifractal cross correlation analysis demonstrates that all underlying fluctuations have almost weak multifractal nature which is also a universal property for data series in addition the empirical relation between scaling exponent derived by dcca and detrended fluctuation analysis dfa h sun h river 2 is confirmed

complexity analysis of the turbulent environmental fluid flow time series abstract we have used the kolmogorov complexities sample and permutation entropies to quantify the randomness degree in river flow time series of two mountain rivers in bosnia and herzegovina representing the turbulent environmental fluid for the period 1926 1990 in particular we have examined the monthly river flow time series from two rivers the miljacka and the bosnia in the mountain part of their flow and then calculated the kolmogorov complexity kl based on the lempel ziv algorithm lza lower kll and upper klu sample entropy se and permutation entropy pe values for each time series the results indicate that the kll klu se and pe values in two rivers are close to each other regardless of the amplitude differences in their monthly flow rates we have illustrated the changes in mountain river flow complexity by experiments using i the data set for the bosnia river and ii anticipated human activities and projected climate changes we have explored the sensitivity of considered measures in dependence on the length of time series in addition we have divided the period 1926 1990 into three subintervals a 1926 1945 b 1946 1965 c 1966 1990 and calculated the kll klu se pe values for the various time series in these subintervals it is found that during the period 1946 1965 there is a decrease in their complexities and corresponding changes in the se and pe in comparison to the period 1926 1990 this complexity loss may be primarily attributed to i human interventions after the second world war on these two rivers because of their use for water consumption and ii climate change in recent times

statistical properties of the volatility of price fluctuations we study the statistical properties of volatility measured by locally averaging over a time window t the absolute value of price changes over a short time interval ensuremath delta t we analyze the s 500 stock index for the 13 year period jan 1984 to dec 1996 we find that the cumulative distribution of the volatility is consistent with a power law asymptotic behavior characterized by an exponent ensuremath mu ensuremath approx 3 similar to what is found for the distribution of price changes the volatility distribution retains the same functional form for a range of values of t further we study the volatility correlations by using the power spectrum analysis both methods support a power law decay of the correlation function and give consistent estimates of the relevant scaling exponents also both methods show the presence of a crossover at approximately 1 5 days in addition we extend these results to the volatility of individual companies by analyzing a data base comprising all trades for the largest 500 u s companies over the two year period jan 1994 to dec 1995

scaling analysis of phase fluctuations in experimental three phase flows abstract the characterization of complex patterns arising from three phase e g oil gas water flows is an important problem with significant engineering and industrial applications based solely on measured conductance fluctuation signals from experimental three phase flows we propose a method to characterize and distinguish three commonly observed flow patterns using the phase characterization method we first calculate the instantaneous phase from the signals then through performing a scaling analysis detrended fluctuation analysis dfa we extract scaling behaviors associated with the phase fluctuations and find that the dfa scaling exponent is sensitive to the transition among different flow patterns which can be used to characterize nonlinear dynamics of the three phase flow from a novel perspective we investigate the three phase flow in terms of phase characterization and scaling analysis the results indicate that our method can provide new insights into the exploration of complex mechanism in flow pattern transition the effectiveness of the method is demonstrated and its broader applicability is articulated

chaotic svd method for minimizing the effect of exponential trends in detrended fluctuation analysis abstract the detrended fluctuation analysis dfa and its extensions mf dfa have been used extensively to determine possible long range correlations in self affine signals however recent studies have reported the susceptibility of dfa to trends which give rise to spurious crossovers and prevent reliable estimation of the scaling exponents in this study a smoothing algorithm based on the chaotic singular value decomposition csvd is proposed to minimize the effect of exponential trends and distortion in the log log plots obtained by dfa techniques the effectiveness of the technique is demonstrated on monofractal and multifractal data corrupted with exponential trends

measuring daily value at risk of ssec index a new approach based on multifractal analysis and extreme value theory abstract recent studies in the econophysics literature reveal that price variability has fractal and multifractal characteristics not only in developed financial markets but also in emerging markets taking high frequency intraday quotes of the shanghai stock exchange component ssec index as example this paper proposes a new method to measure daily value at risk var by combining the newly introduced multifractal volatility mfv model and the extreme value theory evt method two var backtesting techniques are then employed to compare the performance of the model with that of a group of linear and nonlinear generalized autoregressive conditional heteroskedasticity garch models the empirical results show the multifractal nature of price volatility in chinese stock market var measures based on the multifractal volatility model and evt method outperform many garch type models at high risk levels

common scaling patterns in intertrade times of u s stocks we analyze the sequence of time intervals between consecutive stock trades of thirty companies representing eight sectors of the u s economy over a period of 4 yrs for all companies we find that i the probability density function of intertrade times may be fit by a weibull distribution ii when appropriately rescaled the probability densities of all companies collapse onto a single curve implying a universal functional form iii the intertrade times exhibit power law correlated behavior within a trading day and a consistently greater degree of correlation over larger time scales in agreement with the correlation behavior of the absolute price returns for the corresponding company and iv the magnitude series of intertrade time increments is characterized by long range power law correlations suggesting the presence of nonlinear features in the trading dynamics while the sign series is anticorrelated at small scales our results suggest that independent of industry sector market capitalization and average level of trading activity the series of intertrade times exhibit possibly universal scaling patterns which may relate to a common mechanism underlying the trading dynamics of diverse companies further our observation of long range power law correlations and a parallel with the crossover in the scaling of absolute price returns for each individual stock support the hypothesis that the dynamics of transaction times may play a role in the process of price formation

magnitude and sign scaling in power law correlated time series a time series can be decomposed into two sub series a magnitude series and a sign series here we analyze separately the scaling properties of the magnitude series and the sign series using the increment time series of cardiac interbeat intervals as an example we 6nd that time series having identical distributions and long range correlation properties can exhibit quite di8erent temporal organizations of the magnitude and sign sub series from the cases we study it follows that the long range correlations in the magnitude series indicate nonlinear behavior speci6cally our results suggest that the correlation exponent of the magnitude series is a monotonically increasing function of the multifractal spectrum width of the original series on the other hand the sign series mainly relates to linear properties of the original series we also show that the magnitude and sign series of the heart interbeat interval series can be used for diagnosis purposes c

effect of trends on detrended fluctuation analysis scaling behavior we find that crossovers result from the competition between the scaling of the noise and the apparent scaling of the trend we study how the characteristics of these crossovers depend on i the slope of the linear trend ii the amplitude and period of the periodic trend iii the amplitude and power of the power law trend and iv the length as well as the correlation properties of the noise surprisingly we find that the crossovers in the scaling of noisy signals with trends also follow scaling laws i e long range power law dependence of the position of the crossover on the parameters of the trends we show that the dfa result of noise with a trend can be exactly determined by the superposition of the separate results of the dfa on the noise and on the trend assuming that the noise and the trend are not correlated if this superposition rule is not followed this is an indication that the noise and the superposed trend are not independent so that removing the trend could lead to changes in the correlation properties of the noise in addition we show how to use dfa appropriately to minimize the effects of trends how to recognize if a crossover indicates indeed a transition from one type to a different type of underlying correlation or if the crossover is due to a trend without any transition in the dynamical properties of the noise

a stochastic model of river discharge fluctuations we study the daily riverowuctuations of 30 international rivers using the detrendeductu ation analysis we study the correlations in the magnitudes of riverow increments volatilities and 1nd power law correlations in volatilities for time scales less than 1 year these correlations almost disappear for time scales larger than 1 year using surrogate data test for nonlinearity we show that correlations in the magnitudes of riverowuctuations are a measure for nonlinear ity we propose a simple nonlinear stochastic model for riverowuctuations that reproduces the main scaling properties of the riverow series as well as the correlations and periodicities in the magnitudes of riverow increments according to our model the source of nonlinear ity observed in the data is an interaction between a long term correlated process and the river discharge itself c

evaluation of decay times in coupled spaces reliability analysis of bayeisan decay time estimation this paper discusses quantitative tools to evaluate the reliability of decay time estimates and inter relationships between multiple decay times for estimates made within a bayesian framework previous works xiang and goggans j acoust soc am 110 1415 1424 2001 113 2685 2697 2003 have applied bayesian framework to cope with the demanding tasks in estimating multiple decay times from schroeder decay functions measured in acoustically coupled spaces a parametric model of schroeder decay function xiang j acoust soc am 98 2112 2121 1995 has been used for the bayesian model based analysis the relevance of this work is that architectural acousticians need to know how well determined are the estimated decay times calculated within bayesian framework using schroeder decay function data this paper will first address the estimation of global variance of the residual errors between the schroeder function data and its model moreover this paper discusses how the landscape shape of the posterior probability density function over the decay parameter space influences the individual decay time estimates their associated variances and their inter relationships this paper uses experimental results from measured room impulse responses in real halls to describe a model based sampling method for an efficient estimation of decay times and their individual variances these parameters along with decay times are relevant decay parameters for evaluation and understanding of acoustically coupled spaces

detecting long range correlations with detrended fluctuation analysis abstract we examine the detrended fluctuation analysis dfa which is a well established method for the detection of long range correlations in time series we show that deviations from scaling which appear at small time scales become stronger in higher orders of dfa and suggest a modified dfa method to remove them the improvement is necessary especially for short records that are affected by non stationarities furthermore we describe how crossovers in the correlation behavior can be detected reliably and determined quantitatively and show how several types of trends in the data affect the different orders of dfa

price volume cross correlation analysis of csi300 index futures abstract we investigate the cross correlation between price returns and trading volumes for the china securities index 300 csi300 index futures which are the only stock index futures traded on the china financial futures exchange cffex the basic statistics suggest that distributions of these two time series are not normal but exhibit fat tails based on the detrended cross correlation analysis dcca we obtain that returns and trading volumes are long range cross correlated the existence of multifractality in the cross correlation between returns and trading volumes has been proven with the multifractal detrended cross correlation analysis mfdcca algorithm the multifractal analysis also confirms that returns and trading volumes have different degrees of multifractality we further perform a cross correlation statistic to verify whether the cross correlation significantly exists between returns and trading volumes for csi300 index futures in addition results of the test for lead lag effect demonstrate that contemporaneous cross correlation of return and trading volume series is stronger than cross correlations of leaded or lagged series

scaling multifractality and long range correlations in well log data of large scale porous media abstract three distinct methods namely the spectral density the multifractal random walk approach and the multifractal detrended fluctuation analysis are utilized to study the properties of four distinct types of well logs from three oil and gas fields namely the natural gamma ray emission neutron porosity bulk density and the sonic transient time logs such well logs have never been analyzed by the methods that we utilize in the present study the results indicate that the well logs exhibit multifractal characteristics and the estimated hurst exponents by the three methods are close to each other using multifractal detrended fluctuation analysis and the shuffled and surrogated data we find that the source of multifractality is due to both broad probability density functions of the data and long range correlations in them the correlations are persistent and are characterized by a hurst exponent h 0 5 despite very significant differences in the geology of the three reservoirs ranging from shaly sands to fractured carbonate reservoirs there is a rough universality in the log data in that the hurst exponents for all the logs vary in a very narrow range

minimizing the effect of trends on detrended fluctuation analysis of long range correlated noise abstract detrended fluctuation analysis dfa has been proposed as a robust technique to determine possible long range correlations in power law processes phys rev e 49 1994 1685 1989 however recent studies have reported the susceptibility of dfa to trends phys rev e 64 2001 011114 011133 which give rise to spurious crossovers and prevent reliable estimation of the scaling exponents inspired by these reports we propose a technique based on singular value decomposition svd of the trajectory matrix to minimize the effect of linear power law periodic and also quasi periodic trends superimposed on long range correlated power law noise the effectiveness of the technique is demonstrated on publicly available data sets phys rev e 64 2001 011114 011133

comment on optimum absorption and aperture parameters for realistic coupled volume spaces determined from computational analysis and subjective testing results j acoust soc am 127 223 232 2010 a recent paper d t bradley and l m wang j acoust soc am 127 223 232 2010 has reported inconsistencies between the results of two different approaches for characterizing non exponential decays in coupled volume systems this letter aims to expose the origin of these inconsistencies which are due to a limitation in the methodology utilized for the analysis presented in the paper referenced above

testing for the source of multifractality in water level records abstract the multifractality in hydrologic data has been studied in many literatures but few literatures focus on its source which is helpful for understanding the underlying mechanisms that generate hydrologic data we propose a hypothesis testing procedure for the source of multifractality in water level records based on the empirical distributions of generalized hurst exponent estimated from a set of shuffled or surrogate series the proposed hypothesis testing procedure can show more details about the source of multifractality than previous methods with some statistics especially about the effects of large and small fluctuations on multifractality the generalized hurst exponents are estimated via multifractal detrended fluctuation analysis the data set contains about two million high frequency water level records of a northern china river at its 10 observation stations the testing results show that the multifractality in water levels is mainly caused by nonlinear correlations in small fluctuations and linear correlations in small and large fluctuations and is also related to the probability distribution of small fluctuations this conclusion is validated via some constructed semi surrogate series and values of statistics for source testing

multifractality of stock markets based on cumulative distribution function and multiscale multifractal analysis abstract considering the diverse application of multifractal techniques in natural scientific disciplines this work underscores the versatility of multiscale multifractal detrended fluctuation analysis mma method to investigate artificial and real world data sets the modified mma method based on cumulative distribution function is proposed with the objective of quantifying the scaling exponent and multifractality of nonstationary time series it is demonstrated that our approach can provide a more stable and faithful description of multifractal properties in comprehensive range rather than fixing the window length and slide length our analyzes based on cdf mma method reveal significant differences in the multifractal characteristics in the temporal dynamics between us and chinese stock markets suggesting that these two stock markets might be regulated by very different mechanism the cdf mma method is important for evidencing the stable and fine structure of multiscale and multifractal scaling behaviors and can be useful to deepen and broaden our understanding of scaling exponents and multifractal characteristics

effect of nonstationarities on detrended fluctuation analysis detrended fluctuation analysis dfa is a scaling analysis method used to quantify long range power law correlations in signals many physical and biological signals are noisy heterogeneous and exhibit different types of nonstationarities which can affect the correlation properties of these signals we systematically study the effects of three types of nonstationarities often encountered in real data specifically we consider nonstationary sequences formed in three ways i stitching together segments of data obtained from discontinuous experimental recordings or removing some noisy and unreliable parts from continuous recordings and stitching together the remaining parts a cutting procedure commonly used in preparing data prior to signal analysis ii adding to a signal with known correlations a tunable concentration of random outliers or spikes with different amplitudes and iii generating a signal comprised of segments with different properties e g different standard deviations or different correlation exponents we compare the difference between the scaling results obtained for stationary correlated signals and correlated signals with these three types of nonstationarities we find that introducing nonstationarities to stationary correlated signals leads to the appearance of crossovers in the scaling behavior and we study how the characteristics of these crossovers depend on a the fraction and size of the parts cut out from the signal b the concentration of spikes and their amplitudes c the proportion between segments with different standard deviations or different correlations and d the correlation properties of the stationary signal we show how to develop strategies for preprocessing raw data prior to analysis which will minimize the effects of nonstationarities on the scaling properties of the data and how to interpret the results of dfa for complex signals with different local characteristics

a multi rate decay model to predict energy based acoustic parameters in churches multi rate decays are sometimes observed in room acoustics appearing when markedly different volumes are coupled together and resulting in nonlinear decay curves such behavior appears in several churches at the very beginning of the decay process although in conditions which cannot be explicitly referred to as coupling phenomena consequently multi rate exponential decays may be suitable to model energy distribution in this group of buildings providing a more elegant and easily applicable set of equations in place of a previously defined linear model used to adapt barron s revised theory the paper shows that the multi rate approach ensures ease of calculation without significant loss in accuracy in predicting energy based acoustic parameters

the moving averages demystified a common method in technical analysis is the construction of moving averages along time series of stock prices we show that they present a practical interest for physicists and raise new questions on fundamental ground indeed self ane signals characterized by a dened roughness exponent h can be investigated through moving averages the density of crossing points between two moving averages is shown to be a measure of long range power law correlations in a signal finally we present a specic transform with which various structures in a signal e g trends cycles noise etc can be investigated in a systematic way c 1999 elsevier science

evaluation of decay times in coupled spaces an efficient search algorithm within the bayesian framework this paper discusses an efficient method for evaluating multiple decay times within the bayesian framework previous works n xiang and p m goggans j acoust soc am 110 1415 1424 2001 113 2685 2697 2003 n xiang p m goggans t jasa and m kleiner 117 3707 3715 2005 have applied the bayesian inference to cope with demanding tasks in estimating multiple decay times from schroeder decay functions measured or calculated in acoustically coupled spaces since then a number of recent works call for efficient estimation methods within the bayesian framework an efficient analysis is of practical significance for better understanding and modeling the sound energy decay process in acoustically coupled spaces or even in single spaces for reverberation time estimation this paper will first formulate the bayesian posterior probability distribution function ppdf in a matrix form to reduce the dimensionality as applied to the decay time evaluation based on existence of only global extremes o

evaluation of decay times in coupled spaces bayesian parameter estimation determination of sound decay times in coupled spaces often demands considerable effort based on schroeder s backward integration of room impulse responses it is often difficult to distinguish different portions of multirate sound energy decay functions a model based parameter estimation method using bayesian probabilistic inference proves to be a powerful tool for evaluating decay times a decay model due to one of the authors n xiang j acoust soc am 98 2112 2121 1995 is extended to multirate decay functions following a summary of bayesian model based parameter estimation the present paper discusses estimates in terms of both synthesized and measured decay functions no careful estimation of initial values is required in contrast to gradient based approaches the resulting robust algorithmic estimation of more than one decay time from experimentally measured decay functions is clearly superior to the existing nonlinear regression approach