A statistical room impulse response model with frequency dependent reverberation time for single-microphone late reverberation suppression

2011 ◽  
Author(s):  
Jan S. Erkelens ◽  
Richard Heusdens
Keyword(s):  
Impulse Response ◽  
Response Model ◽  
Reverberation Time ◽  
Frequency Dependent

Quantifying Non-Linearity in Early Decay Curves of Measured and Computer-Modeled Room Impulse Responses of a Highly Non-Diffuse Room Exhibiting Flutter Echo

2020 ◽  
Author(s):  
Heather L. Lai ◽  
Brian Hamilton
Keyword(s):  
Linear Regression ◽  
Computer Modeling ◽  
Impulse Response ◽  
Decay Curve ◽  
Sound Field ◽  
Energy Decay ◽  
Room Acoustics ◽  
Reverberation Time ◽  
Sound Fields ◽  
Regression Curves

Abstract This paper investigates the use of two room acoustics metrics designed to evaluate the degree to which the linearity assumptions of the energy density curves are valid. The study focuses on measured and computer-modeled energy density curves derived from the room impulse response of a space exhibiting a highly non-diffuse sound field due to flutter echo. In conjunction with acoustical remediation, room impulse response measurements were taken before and after the installation of the acoustical panels. A very dramatic decrease in the reverberation time was experienced due to the addition of the acoustical panels. The two non-linearity metrics used in this study are the non-linearity parameter and the curvature. These metrics are calculated from the energy decay curves computed per octave band, based on the definitions presented in ISO 3382-2. The non-linearity parameter quantifies the deviation of the EDC from a straight line fit used to generated T20 and T30 reverberation times. Where the reverberation times are calculated based on a linear regression of the data relating to either −5 to −25 dB for T20 or −5 to −35 dB for T30 reverberation time calculations. This deviation is quantified using the correlation coefficient between the energy decay curve and the linear regression for the specified data. In order to graphically demonstrate these non-linearity metrics, the energy decay curves are plotted along with the linear regression curves for the T20 and T30 reverberation time for both the measured data and two different room acoustics computer-modeling techniques, geometric acoustics modeling and finite-difference wave-based modeling. The intent of plotting these curves together is to demonstrate the relationship between these metrics and the energy decay curve, and to evaluate their use for quantifying degree of non-linearity in non-diffuse sound fields. Observations of these graphical representations are used to evaluate the accuracy of reverberation time estimations in non-diffuse environments, and to evaluate the use of these non-linearity parameters for comparison of different computer-modeling techniques or room configurations. Using these techniques, the non-linearity parameter based on both T20 and T30 linear regression curves and the curvature parameter were calculated over 250–4000 Hz octave bands for the measured and computer-modeled room impulse response curves at two different locations and two different room configurations. Observations of these calculated results are used to evaluate the consistency of these metrics, and the application of these metrics to quantifying the degree of non-linearity of the energy decay curve derived from a non-diffuse sound field. These calculated values are also used to evaluate the differences in the degree of diffusivity between the measured and computer-modeled room impulse response. Acoustical computer modeling is often based on geometrical acoustics using ray-tracing and image-source algorithms, however, in non-diffuse sound fields, wave based methods are often able to better model the characteristic sound wave patterns that are developed. It is of interest to study whether these improvements in the wave based computer-modeling are also reflected in the non-linearity parameter calculations. The results showed that these metrics provide an effective criteria for identifying non-linearity in the energy decay curve, however for highly non-diffuse sound fields, the resulting values were found to be very sensitive to fluctuations in the energy decay curves and therefore, contain inconsistencies due to these differences.

Measurement of Transmission Properties of HBC Channel and Its Impulse Response Model

2016 ◽  
Vol 65 (1) ◽  
pp. 177-188 ◽  
Author(s):  
Jung-Hwan Hwang ◽  
Tae-Wook Kang ◽  
Youn-Tae Kim ◽  
Seong-Ook Park
Keyword(s):  
Impulse Response ◽  
Response Model ◽  
Transmission Properties

A Frequency-Domain INS/GPS Dynamic Response Method for Bridging GPS Outages

2010 ◽  
Vol 63 (4) ◽  
pp. 627-643 ◽  
Author(s):  
Mohammed El-Diasty ◽  
Spiros Pagiatakis
Keyword(s):  
Neural Network ◽  
Transfer Function ◽  
Dynamic Response ◽  
Dynamic System ◽  
Least Squares ◽  
Frequency Domain ◽  
Impulse Response ◽  
Time Domain ◽  
Response Model ◽  
Response Method

We develop a new frequency-domain dynamic response method to model integrated Inertial Navigation System (INS) and Global Positioning System (GPS) architectures and provide an accurate impulse-response-based INS-only navigation solution when GPS signals are denied (GPS outages). The input to such a dynamic system is the INS-only solution and the output is the INS/GPS integration solution; both are used to derive the transfer function of the dynamic system using Least Squares Frequency Transform (LSFT). The discrete Inverse Least Squares Frequency Transform (ILSFT) of the transfer function is applied to estimate the impulse response of the INS/GPS system in the time domain. It is shown that the long-term motion dynamics of a DQI-100 IMU/Trimble BD950 integrated system are recovered by 72%, 42%, 75%, and 40% for north and east velocities, and north and east positions respectively, when compared with the INS-only solution (prediction mode of the INS/GPS filter). A comparison between our impulse response model and the current state-of-the-art time-domain feed-forward neural network shows that the proposed frequency-dependent INS/GPS response model is superior to the neural network model by about 26% for 2D velocities and positions during GPS outages.

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