Showing papers on "Impulse response published in 2010"

Source location using time‐reverse imaging

Abstract: We present the chain of time-reverse modeling, image space wavefield decomposition and several imaging conditions as a migration-like algorithm called time-reverse imaging. The algorithm locates subsurface sources in passive seismic data and diffractors in active data. We use elastic propagators to capitalize on the full waveforms available in multicomponent data, although an acoustic example is presented as well. For the elastic case, we perform wavefield decomposition in the image domain with spatial derivatives to calculate P and S potentials. To locate sources, the time axis is collapsed by extracting the zero-lag of auto and cross-correlations to return images in physical space. The impulse response of the algorithm is very dependent on acquisition geometry and needs to be evaluated with point sources before processing field data. Band-limited data processed with these techniques image the radiation pattern of the source rather than just the location. We present several imaging conditions but we imagine others could be designed to investigate specific hypotheses concerning the nature of the source mechanism. We illustrate the flexible technique with synthetic 2D passive data examples and surface acquisition geometry specifically designed to investigate tremor type signals that are not easily identified or interpreted in the time domain.

Neural responses to uninterrupted natural speech can be extracted with precise temporal resolution.

Abstract: The human auditory system has evolved to efficiently process individual streams of speech. However, obtaining temporally detailed responses to distinct continuous natural speech streams has hitherto been impracticable using standard neurophysiological techniques. Here a method is described which provides for the estimation of a temporally precise electrophysiological response to uninterrupted natural speech. We have termed this response AESPA (Auditory Evoked Spread Spectrum Analysis) and it represents an estimate of the impulse response of the auditory system. It is obtained by assuming that the recorded electrophysiological function represents a convolution of the amplitude envelope of a continuous speech stream with the to-be-estimated impulse response. We present examples of these responses using both scalp and intracranially recorded human EEG, which were obtained while subjects listened to a binaurally presented recording of a male speaker reading naturally from a classic work of fiction. This method expands the arsenal of stimulation types that can now be effectively used to derive auditory evoked responses and allows for the use of considerably more ecologically valid stimulation parameters. Some implications for future research efforts are presented.

On vector autoregressive modeling in space and time

Abstract: Despite the fact that it provides a potentially useful analytical tool, allowing for the joint modeling of dynamic interdependencies within a group of connected areas, until lately the VAR approach had received little attention in regional science and spatial economic analysis. This paper aims to contribute in this field by dealing with the issues of parameter identification and estimation and of structural impulse response analysis. In particular, there is a discussion of the adaptation of the recursive identification scheme (which represents one of the more common approaches in the time series VAR literature) to a space-time environment. Parameter estimation is subsequently based on the Full Information Maximum Likelihood (FIML) method, a standard approach in structural VAR analysis. As a convenient tool to summarize the information conveyed by regional dynamic multipliers with a specific emphasis on the scope of spatial spillover effects, a synthetic space-time impulse response function (STIR) is introduced, portraying average effects as a function of displacement in time and space. Asymptotic confidence bands for the STIR estimates are also derived from bootstrap estimates of the standard errors. Finally, to provide a basic illustration of the methodology, the paper presents an application of a simple bivariate fiscal model fitted to data for Italian NUTS 2 regions.

MIMO Radar Waveform Design in Colored Noise Based on Information Theory

Abstract: In this paper, we consider multiple input multiple output (MIMO) radar waveform design in colored noise. Two information theoretic measures are used as criterions for optimal waveform design under transmitted power constraint. The first one is by maximizing the mutual information between target impulse response and target echoes; the second one is by maximizing the relative entropy between two hypotheses: in the first hypothesis we assume the target is not present in the echoes while in the second hypothesis we assume the target exists in the echoes. We derive optimal solutions for both cases. Interestingly, both optimal solutions require that transmitted waveform should “match” with the target and noise. However, the optimal solutions of the two problems lead to different power allocation strategies.

Frequency-Wavelet Domain Deconvolution for terahertz reflection imaging and spectroscopy.

Abstract: In terahertz reflection imaging, a deconvolution process is often employed to extract the impulse function of the sample of interest. A band-pass filter such as a double Gaussian filter is typically incorporated into the inverse filtering to suppress the noise, but this can result in over-smoothing due to the loss of useful information. In this paper, with a view to improving the calculation of terahertz impulse response functions for systems with a low signal to noise ratio, we propose a hybrid Frequency-Wavelet Domain Deconvolution (FWDD) for terahertz reflection imaging. Our approach works well; it retrieves more accurate impulse response functions than existing approaches and these impulse functions can then also be used to better extract the terahertz spectroscopic properties of the sample.

Linear Programming Algorithms for Sparse Filter Design

Abstract: In designing discrete-time filters, the length of the impulse response is often used as an indication of computational cost. In systems where the complexity is dominated by arithmetic operations, the number of nonzero coefficients in the impulse response may be a more appropriate metric to consider instead, and computational savings are realized by omitting arithmetic operations associated with zero-valued coefficients. This metric is particularly relevant to the design of sensor arrays, where a set of array weights with many zero-valued entries allows for the elimination of physical array elements, resulting in a reduction of data acquisition and communication costs. However, designing a filter with the fewest number of nonzero coefficients subject to a set of frequency-domain constraints is a computationally difficult optimization problem. This paper describes several approximate polynomial-time algorithms that use linear programming to design filters having a small number of nonzero coefficients, i.e., filters that are sparse. Specifically, we present two approaches that have different computational complexities in terms of the number of required linear programs. The first technique iteratively thins the impulse response of a non-sparse filter until frequency-domain constraints are violated. The second minimizes the 1-norm of the impulse response of the filter, using the resulting design to determine the coefficients that are constrained to zero in a subsequent re-optimization stage. The algorithms are evaluated within the contexts of array design and acoustic equalization.

Channel Estimation with Low-Precision Analog-to-Digital Conversion

Abstract: We consider the problem of estimating the impulse response of a dispersive channel when the channel output is sampled using a low-precision analog-to-digital converter (ADC). While traditional channel estimation techniques require about 6 bits of ADC precision to approach full-precision performance, we are motivated by applications to multiGigabit communication, where we may be forced to use much lower precision (e.g., 1-3 bits) due to considerations of cost, power, and technological feasibility. We show that, even with such low ADC precision, it is possible to attain near full-precision performance using closed-loop estimation, where the ADC input is dithered and scaled. The dither signal is obtained using linear feedback based on the Minimum Mean Squared Error (MMSE) criterion. The dither feedback coefficients and the scaling gains are computed offline using Monte Carlo simulations based on a statistical model for the channel taps, and are found to work well over wide range of channel variations.

Modeling the ultra-wideband outdoor channel: Measurements and parameter extraction method

Abstract: This paper presents results from an outdoor measurement campaign for ultra-wideband channels at gas stations. The results are particularly relevant for "infostations" where large amounts of data are downloaded to a user within a short period of time. We describe the measurement setup and present a novel high-resolution algorithm that allows the identification of the scatterers that give rise to multipath components. As input, the algorithm uses measurements of the transfer function between a single-antenna transmitter and a long uniform linear virtual array as receiver. The size of the array ensures that the incoming waves are spherical, which improves the estimation accuracy of scatterer locations. Insight is given on how these components can be tracked in the impulse response of a spatially varying terminal. We then group the detected scatterers into clusters, and investigate the angular power variations of waves arriving at the receiver from the clusters. This defines the cluster's "radiation pattern." Using sample measurements we show how obstacles obstruct the line-of-sight component -- a phenomenon commonly referred to as "shadowing." We compare the measurement data in the shadowing regions (locations of the receiver experiencing shadowing) with the theoretical results predicted by diffraction theory and find a good match between the two.

Room Impulse Response Shortening/Reshaping With Infinity- and $p$ -Norm Optimization

Abstract: The purpose of room impulse response (RIR) shortening and reshaping is usually to improve the intelligibility of the received signal by prefiltering the source signal before it is played with a loudspeaker in a closed room. In an alternative, but mathematically equivalent setting, one may aim to postfilter a recorded microphone signal to remove audible echoes. While least-squares methods have mainly been used for the design of shortening/reshaping filters for RIRs until now, we propose to use the infinity- or p-norm as optimization criteria. In our method, design errors will be uniformly distributed over the entire temporal range of the shortened/reshaped global impulse response. In addition, the psychoacoustic property of masking effects is considered during the filter design, which makes it possible to significantly reduce the filter length, compared to standard approaches, without affecting the perceived performance.

Impulse Response of Civil Structures from Ambient Noise Analysis

Abstract: Increased monitoring of civil structures for response to earthquake motions is fundamental to reducing seismic risk. Seismic monitoring is difficult because typically only a few useful, intermediate to large earthquakes occur per decade near instrumented structures. Here, we demonstrate that the impulse response function (IRF) of a multistory building can be generated from ambient noise. Estimated shear-wave velocity, attenuation values, and resonance frequencies from the IRF agree with previous estimates for the instrumented University of California, Los Angeles, Factor building. The accuracy of the approach is demonstrated by predicting the Factor building’s response to an M 4.2 earthquake. The methodology described here allows for rapid, noninvasive determination of structural parameters from the IRFs within days and could be used for state-of-health monitoring of civil structures (buildings, bridges, etc.) before and/or after major earthquakes.

Real-time near-field acoustic holography for continuously visualizing nonstationary acoustic fields

Abstract: Near-field acoustic holography (NAH) is an effective tool for visualizing acoustic sources from pressure measurements made in the near-field of sources using a microphone array. The method involving the Fourier transform and some processing in the frequency-wavenumber domain is suitable for the study of stationary acoustic sources, providing an image of the spatial acoustic field for one frequency. When the behavior of acoustic sources fluctuates in time, NAH may not be used. Unlike time domain holography or transient method, the method proposed in the paper needs no transformation in the frequency domain or any assumption about local stationary properties. It is based on a time formulation of forward sound prediction or backward sound radiation in the time-wavenumber domain. The propagation is described by an analytic impulse response used to define a digital filter. The implementation of one filter in forward propagation and its inverse to recover the acoustic field on the source plane implies by simulations that real-time NAH is viable. Since a numerical filter is used rather than a Fourier transform of the time-signal, the emission on a point of the source may be rebuilt continuously and used for other post-processing applications.

Adaptive combination of subband adaptive filters for acoustic echo cancellation

Abstract: In hands-free telephones and teleconferencing systems, acoustic echo cancellers are required, which are often implemented by adaptive filters. In these applications, the speech input signal of the adaptive filter is highly correlated and the impulse response of the echo path is very long. These characteristics will slow down the convergence rate of the adaptive filter if the well-known normalized least-mean-square (NLMS) algorithm is used. The normalized subband adaptive filter (NSAF) offers a good solution to this problem because of its decorrelating property. However, similar to the NLMS-based adaptive filter, the NSAF requires a tradeoff between fast convergence rate and small steady-state mean-square error (MSE). In this paper, we propose an adaptive combination scheme to address this tradeoff. The combination is carried out in subband domain and the mixing parameter that controls the combination is adapted by means of a stochastic gradient algorithm which employs the sum of squared subband errors as the cost function. The performance of the proposed combination scheme is evaluated in the context of acoustic echo cancellation (AEC). Experimental results show that the combination scheme can obtain both fast convergence rate and small steady-state MSE.

Regularized estimation of sums of exponentials in spaces generated by stable spline kernels

Abstract: A new nonparametric paradigm to model identification has been recently introduced in the literature. Instead of adopting finite-dimensional models of the system transfer function, the system impulse response is searched for within an infinite dimensional space using regularization theory. The method exploits the so called stable spline kernels which are associated with hypothesis spaces embedding information on both regularity and stability of the impulse response. In this paper, the potentiality of this approach is studied with respect to the reconstruction of sums of exponentials. In particular, first, we characterize the learning rates of our estimator in reconstructing such class of functions also exploiting recent advances in statistical learning theory. Then, we use Monte Carlo studies to illustrate the definite advantages of this new nonparametric approach over classical parametric prediction error methods in terms of accuracy in impulse responses reconstruction.

Modeling the Ultra-Wideband Outdoor Channel: Model Specification and Validation

Abstract: In this paper we establish a geometry-based stochastic ultra-wideband channel model for gas stations. We statistically describe the two-dimensional spatial location and power of clustered scatterers, and the shape of their visibility and shadowing regions. We also separately model the diffuse part of the impulse response (i.e., the part that cannot be explained by the scatterers' multipath components), and show that its amplitude fading statistics can be best described by a Weibull distribution with a delay dependent beta-parameter. A step-by-step implementation recipe demonstrates how the model can be built. Finally, we validate our model by comparing simulated and measured channel parameters such as the rms delay spread.

Design of surge tank for water supply systems using the impulse response method with the GA algorithm

Abstract: The impact of the surge tank has been incorporated into the platform of the impulse response method. The impedance functions for pipeline systems equipped with a surge tank were also derived. Hydraulic transients could be efficiently analyzed by the developed method. The simulation of normalized pressure variation using the method of characteristics and the impulse response method shows good agreement only in the condition of an identical computational interval between pipeline elements and that of the surge tank connector. The important numerical issue, the Courant number condition, of the traditional grid-based approaches can introduce substantial difficulty for optimization of surge tank parameters. The surge tank design could be performed by incorporation of the impulse response method with the Genetic Algorithm (GA). The objective functions for the surge tank design can be made using the pressure-head response at any point along the pipeline system while considering both the security and cost of the system. Substantial flexibility in the design of surge tank parameters, such as the location in the pipeline, the length of the connector, and the diameters for the connector and the surge tank can be found during the optimization procedure.

An Impulse Response Function for Evaluation of UWB SAR Imaging

Abstract: Based on analysis of a point target imaged by different synthetic aperture radar (SAR) systems, the commonly used impulse response function in SAR Imaging (IRF-SAR)-a two-dimensional (2-D) sinc function-is shown to be inappropriate for ultrawideband-ultrawidebeam (UWB) SAR systems utilizing a large fractional signal bandwidth and a wide antenna beamwidth. As a consequence, the applications of the 2-D sinc function such as image quality measurements and spatial resolution estimations are limited to narrowband-narrowbeam (NB) SAR systems exploiting a small fractional signal bandwidth and a narrow antenna beamwidth. In this paper, a more general IRF-SAR, which aims at UWB SAR systems, is derived with an assumption of flat two-dimensional (2-D) Fourier transform (FT) of a SAR image and called IRF-USAR. However, the derived IRF-USAR is also valid for NB SAR systems.

Resonance phenomena in the human auditory cortex: individual resonance frequencies of the cerebral cortex determine electrophysiological responses

Abstract: The brain can be considered a dynamical system which is able to oscillate at multiple frequencies. To study the brain's preferred oscillation frequencies, the resonance frequencies in the frequency response of the system can be assessed by stimulating the brain at various stimulation frequencies. Furthermore, the event-related potential (ERP) can be considered as the brain's impulse response. For linear dynamical systems, the frequency response should be equivalent to the frequency transform of the impulse response. The present study test whether this fundamental relation is also true for the frequency transform of the ERP and the frequency response of the brain. Results show that the spectral characteristics of both impulse and frequency response in the gamma frequency range are significantly correlated. Thus, we speculate that the resonance frequencies determine the frequency spectrum of the impulse response. This, in turn, implies that both measures are determined by the same, individually specific, neuronal generator mechanisms.

Digital filters with sparse coefficients

Abstract: Is sparsity an issue in filter design problems? and why is it important? How a digital filter can be designed to have a sparse impulse response for efficient implementation while achieving improved performance relative to its non-sparse counterpart? In an attempt to address these questions, this paper comes up with a design technique for optimal linear-phase FIR filters with sparse impulse responses.

Method for dereverberation and noise reduction using spherical microphone arrays

Abstract: A method for dereverberation and noise reduction is presented. The method is designed for a spherical microphone array, and formulated in the spherical harmonics domain, based on an acoustic model that is also formulated in the spherical harmonics domain. The novelty in the proposed method is the dereverberation process, which exploits the useful formulation in the spherical harmonics domain, which facilitates dereverberation by employing DOA estimation, rather that room impulse response identification. Noise reduction is further performed by a linearly constrained minimum variance filter, where the array output power is minimized with constraint of distortionless response to the direct sound. The paper concludes with a simulation investigation and comparison to theoretical results.

Signal-based performance evaluation of dereverberation algorithms

Abstract: We address the measurement of reverberation in terms of the (DRR) in the context of the assessment of dereverberation algorithms for which we wish to quantify the level of reverberation before and after processing. The DRR is normally calculated from the impulse response of the reverberating system. However, several important dereverberation algorithms involve nonlinear and/or time-varying processing and therefore their effect cannot conveniently be represented in terms of modifications to the impulse response of the reverberating system. In such cases, we show that a good estimate of DRR can be obtained from the input/output signals alone using the Signal-to-Reverberant Ratio (SRR) only if the source signal is spectrally white and correctly normalized. We study alternative normalization schemes and conclude by showing a least squares optimal normalization procedure for estimating DRR using signal-based SRR measurement. Simulation results illustrate the accuracy of DRR estimation using SRR.

Estimation of State-Of-Health in Batteries

Abstract: Methods and apparatuses for estimation of a state-of-health in a battery are disclosed. An example method comprises: determining if a terminal voltage of the chemical battery differs from a calculated terminal voltage; in response to determining that the terminal voltage of the chemical battery differs from the calculated terminal voltage, calculating a range of voltages by taking the convolution of a terminal current of the chemical battery with a range of impulse responses from a look up table of impulse responses corresponding to different states-of-health; comparing the terminal voltage of the chemical battery with the range of calculated voltages to determine a second impulse response that corresponds to the case where the terminal voltage matches the calculated voltage; and using the look up table of impulse responses corresponding to different states-of-health to determine the state-of-health of the chemical battery from the second impulse response. Other embodiments are described and claimed.

Adaptive Sampling Rate Correction for Acoustic Echo Control in Voice-Over-IP

Abstract: Hands-free terminals for speech communication employ adaptive filters to reduce echoes resulting from the acoustic coupling between loudspeaker and microphone. When using a personal computer with commercial audio hardware for teleconferencing, a sampling frequency offset between the loudspeaker output D/A converter and the microphone input A/D converter often occurs. In this case, state-of-the-art echo cancellation algorithms fail to track the correct room impulse response. In this paper, we present a novel least mean square (LMS-type) adaptive algorithm to estimate the frequency offset and resynchronize the signals using arbitrary sampling rate conversion. In conjunction with a normalized LMS-type adaptive filter for room impulse response tracking, the proposed system widely removes the deteriorating effects of a frequency offset up to several Hz and restores the functionality of echo cancellation.

Method for testing assembly performance of rotor of aircraft engine

Abstract: The invention discloses a method for testing the assembly performance of a rotor of an aircraft engine, which comprises the following steps of: firstly exciting and vibrating a rotor of an aircraft engine with a vibration exciter; obtaining a multiple carrier-coupled impulse response signal of the rotor of the aircraft engine with a vibrating sensor and signal-acquiring system software; analyzing the obtained multiple carrier-coupled impulse response signal of the rotor of the aircraft engine by means of dual-tree complex wavelet transform to obtain eight signal carrier-coupled impulse response signals of the rotor of the aircraft engine; and distilling the average assembly performance index of the obtained eight signal carrier-coupled impulse response signals of the rotor of the aircraft engine, wherein the assembly performance of the rotor of the aircraft engine is judged to be qualified if the obtained average assembly performance index is larger than or equal to 10, and the assembly performance of the rotor of the aircraft engine is judged not to be qualified if the obtained average assembly performance index is less than 10, so that the rotor needs to be repaired.

Matching pursuit: Evaluation and implementatio for LTE channel estimation

Abstract: The emerging research field of compressed sensing (CS) promises better signal reconstruction out of fewer measurements if a sparse representation of the signal exists. Since wireless broadband channels often exhibit a sparse impulse response, CS reconstruction algorithms were proposed for channel estimation. In this paper, a hardware architecture for channel estimation using the matching pursuit algorithm is presented. The reference design targets the 3GPP LTE standard with a channel bandwidth of up to 20 MHz. Achievable performance gains over least squares channel estimation are illustrated by means of simulations. The costs in terms of chip area and reconstruction time for 180 nm CMOS technology are presented together with an analysis of the tradeoff between hardware complexity and reconstruction performance.

Adaptive waveform design for range-spread target tracking

Abstract: Suboptimal waveform design based on the wide sense stationary-uncorrelated scattering target impulse response model is proposed, which reduces the minimum mean square error of estimation. Simulation results show that the waveform designed by the proposed method performs better than the traditional waveform.

Localization in Underwater Dispersive Channels Using the Time-Frequency-Phase Continuity of Signals

Abstract: Time-frequency representations constitute the main tool for analysis of nonstationary signals arising in real-life systems. One of the most challenging applications of time-frequency representations deal with the analysis of the underwater acoustic signals. Recently, the interest for dispersive channels increased mainly due to the presence of the wide band nonlinear effect at very low frequencies. That is, if we intend to establish an underwater communication link at low frequencies, the dispersion phenomenon has to be taken into account. In such conditions, the application of the conventional time-frequency tools could be a difficult task, mainly because of the nonlinearity and the closeness of the time-frequency components of the impulse response. Moreover, the channel being unknown, any assumption about the instantaneous frequency laws characterizing the channel could not be approximate. In this paper, we introduce a new time-frequency analysis tool that aims to extract the time-frequency components of the channel impulse response. The main feature of this technique is the joint use of time-amplitude, time-frequency, and time-phase information. Tests provided for realistic scenarios and real data illustrate the potential and the benefits of the proposed approach.

Neural–Genetic Synthesis for State-Space Controllers Based on Linear Quadratic Regulator Design for Eigenstructure Assignment

Abstract: Toward the synthesis of state-space controllers, a neural-genetic model based on the linear quadratic regulator design for the eigenstructure assignment of multivariable dynamic systems is presented. The neural-genetic model represents a fusion of a genetic algorithm and a recurrent neural network (RNN) to perform the selection of the weighting matrices and the algebraic Riccati equation solution, respectively. A fourth-order electric circuit model is used to evaluate the convergence of the computational intelligence paradigms and the control design method performance. The genetic search convergence evaluation is performed in terms of the fitness function statistics and the RNN convergence, which is evaluated by landscapes of the energy and norm, as a function of the parameter deviations. The control problem solution is evaluated in the time and frequency domains by the impulse response, singular values, and modal analysis.

Impulse Response Model and Parameters for Indoor Channel Modeling at 60GHz

Abstract: In order to realize very high throughput wireless local area network (WLAN) systems, which demand multi Gbps capacities/bitrates, the IEEE802.11.TGad (Next generation WLAN standard at 60GHz band) is in the standardization process. To evaluate and select the proposed physical layer modulation and coding schemes in the standardization process, channel models for each specified usage model are required. In this paper, suitable impulse channel models for that purpose are proposed. Propagation measurements are carried out in a living room environment in the 60GHz band. To cover various usage cases, line-of-sight (LOS) and non-line-of-site (NLOS) scenarios and three different signal transmission polarization types are examined, and each channel model's parameters are extracted by statistical analysis. These channel models are useful in designing systems for millimeter-wave wireless communications.

Noisy Data and Impulse Response Estimation

Abstract: This paper investigates the impulse response estimation of linear time-invariant (LTI) systems when only noisy finite-length input-output data of the system is available. The competing parametric candidates are the least square impulse response estimates of possibly different lengths. It is known that the presence of noise prohibits using model sets with large number of parameters as the resulting parameter estimation error can be quite large. Model selection methods acknowledge this problem, hence, they provide metrics to compare estimates in different model classes. Such metrics typically involve a combination of the available least-square output error, which decreases as the number of parameters increases, and a function that penalizes the size of the model. In this paper, we approach the model class selection problem from a different perspective that is closely related to the involved denoising problem. The method primarily focuses on estimating the parameter error in a given model class of finite order using the available least-square output error. We show that such an estimate, which is provided in terms of upper and lower bounds with certain level of confidence, contains the appropriate tradeoffs between the bias and variance of the estimation error. Consequently, these measures can be used as the basis for model comparison and model selection. Furthermore, we demonstrate how this approach reduces to the celebrated AIC method for a specific confidence level. The performance of the method as the noise variance and/or the data length varies is explored, and consistency of the approach as the data length grows is analyzed.

Frequency- and Time-dependent Geometry for Real-time Auralizations

Abstract: The quality of present-day room acoustic simulations stands and falls by the quality of the underlying CAD room models. A ‘high-quality’ room model does not implicate that it has to be highly detailed with a lot of small objects and ornamentation. High accuracy of a model and its auralization is only achieved when basic acoustic principles are regarded. This means for geometrically based simulations that wavelengths from 1.7cm till 17m have to be handled with regard to their reflection/scattering pattern at room or object surfaces. As this spans the dimensions of the majority of known objects, walls etc., only an adapting room model that changes its level of detail accordingly to the incident sound wave frequency can ensure correct results, e.g. for low-frequency specular reflections. When it comes to real-time auralizations, which are used in sophisticated virtual reality systems, another emerging aspect is how to deal with very complex room geometries in limited computation time. Using active frequency dependent geometry has great advantages in this discipline due to much faster simulations when simple geometries with a low polygon count are involved. Lower frequencies typically travel much longer than higher ones in common rooms and thus they produce the majority of computation load which is now significantly reduced, yielding a great speed-up potential. Going on, the introduction of a temporal discretization reducing room model details step by step over the duration of the room impulse response can save valuable computation resources as well. This technique makes use of perceptual characteristics of the human ear, which is not able to distinguish fine structures in the late part of the impulse response. Furthermore, most of the energy in this late part originates from diffuse reflections for which the exact geometry does not matter. Thus, the active geometry model switches to simpler structures for late reflections. In this contribution, the newly developed active geometry model, which uses a frequency and time dependent level of detail, will be presented as well as results from comparative listening tests. These could point out the necessary complexity for the highest detail step as well as the maximally allowed simplification based on human perception.