Impulse Response Data Research Articles

An Acoustical Survey of a Catholic Chapel

The modern-day celebration of the Roman Catholic Mass places an increased emphasis on the speech, music, and participation of the congregation in these ministries. Thus, there is an increasing emphasis on ensuring churches provide acceptable experiences for speech and music. This paper presents the results of an empirical study of a Roman Catholic chapel. The acoustic metrics were determined from impulse response data gathered via microphones recording balloon pops. Sound pressure level measurements were gathered with a sound pressure meter recording a boombox noise source. The balloon pops and boombox noise source were placed in various locations indicative of sources during the celebration of the Mass. Findings include that the RT30 measurements of the chapel were consistent with the EDT values suggesting that early reflections should not provide a significant impact on the perceived reverberation of the chapel. Based on the results, we conclude that the chapel does not have any significant deficiencies in its ability to transmit speech and music. This provides a basis for the chapel administration to invest in a professional assessment as they begin a project to install a new organ and choir loft.

Determination of the sound absorption coefficient of ETFE cushions in-situ using the genetic material optimizer in Odeon®

ETFE cushions are used as cladding in predominantly mid to large size architectural projects. Smaller cushions are about 10 m2 in size and the biggest single cushions can be several hundreds of square metres large. They are composed of ETFE membranes a few hundred of micrometres thick. From a point of view of room acoustics, their acoustic transparency, which increases with decreasing frequency, makes them to effectively act like sound absorbers. Their voluminous nature and transparency make it challenging to measure the sound absorption coefficient of ETFE cushions in a laboratory. Using the genetic material optimizer of Odeon® software, the effective sound absorption coefficient of ETFE cushions in four rooms was extracted from room acoustic parameters, which were in turn derived from measured room impulse response data. The results were compared and interpreted by the help of theoretical modelling and measurements on small cushions in a reverberant room. There is a relatively good agreement between the three types of results, possible causes for the differences are explained in the paper.

A comprehensive multimodal dataset for contactless lip reading and acoustic analysis

Small-scale motion detection using non-invasive remote sensing techniques has recently garnered significant interest in the field of speech recognition. Our dataset paper aims to facilitate the enhancement and restoration of speech information from diverse data sources for speakers. In this paper, we introduce a novel multimodal dataset based on Radio Frequency, visual, text, audio, laser and lip landmark information, also called RVTALL. Specifically, the dataset consists of 7.5 GHz Channel Impulse Response (CIR) data from ultra-wideband (UWB) radars, 77 GHz frequency modulated continuous wave (FMCW) data from millimeter wave (mmWave) radar, visual and audio information, lip landmarks and laser data, offering a unique multimodal approach to speech recognition research. Meanwhile, a depth camera is adopted to record the landmarks of the subject’s lip and voice. Approximately 400 minutes of annotated speech profiles are provided, which are collected from 20 participants speaking 5 vowels, 15 words, and 16 sentences. The dataset has been validated and has potential for the investigation of lip reading and multimodal speech recognition.

Comprehensive Simulation Framework for Space-Air-Ground Integrated Network Propagation Channel Research.

The space-air-ground integrated network (SAGIN) represents a pivotal component within the realm of next-generation mobile communication technologies, owing to its established reliability and adaptable coverage capabilities. Central to the advancement of SAGIN is propagation channel research due to its critical role in aiding network system design and resource deployment. Nevertheless, real-world propagation channel research faces challenges in data collection, deployment, and testing. Consequently, this paper designs a comprehensive simulation framework tailored to facilitate SAGIN propagation channel research. The framework integrates the open source QuaDRiGa platform and the self-developed satellite channel simulation platform to simulate communication channels across diverse scenarios, and also integrates data processing, intelligent identification, algorithm optimization modules in a modular way to process the simulated data. We also provide a case study of scenario identification, in which typical channel features are extracted based on channel impulse response (CIR) data, and recognition models based on different artificial intelligence algorithms are constructed and compared.

Design of a Tripod-Shaped Radiator Patch Antenna for Ultra-Wideband Direction Finding.

As UWB technology develops and devices become smaller, miniaturization techniques for an array antenna system are required. In addition, more in-depth research is needed for UWB direction-finding techniques using channel impulse response (CIR) data. This paper proposes an ultra-wideband (UWB) antenna using a single-radiator multiple-port (SRMP) design for the direction-finding systems of smart devices. The proposed SRMP antenna was designed using a single tripod-shaped patch that can replace the array system. The tripod-shaped radiator was optimized using the edge shape design function to improve its broadband and mutual coupling characteristics. For performance verification, the proposed antenna was fabricated, and the reflection coefficient, mutual coupling, and radiation patterns were measured in a fully anechoic chamber. The proposed antenna has an operating frequency band of 6.1 GHz (from 5.8 GHz to 11.9 GHz) for port 1 and a measured mutual coupling of -14.8 dB at 8 GHz. The SRMP antenna has measured maximum gains of 3.5 dBi for port 1 and 2.9 dBi for port 2. To examine the direction-finding performance, the fabricated antenna was connected to a circuit module with a DW3000 chip, which is widely employed in commercial mobile UWB systems. The direction of arrival (DoA) results using the measured CIR data show root-mean-square (RMS) errors of 1.57° and 4.58° at distances of 30 cm and 60 cm.

Indoor UWB Positioning and Position Tracking Data Set

Indoor positioning has become a hot topic in various fields, such as industry, healthcare, and commerce. Ultra-wideband (UWB) radio technology provides a cost-effective solution for range-based positioning, offering exceptionally high accuracy and precision. Its ultra-high temporal resolution enables range measurements with accuracy of a few centimeters. To develop and evaluate range-based positioning systems, we collected measurements in four different indoor environments using eight fixed devices and one mobile positioning device. To eliminate the fluctuation of walking speed from the data, we pre-defined a path in each indoor environment, similar to the human walking path, which was sampled at equidistant positions. We collected multiple range measurements and channel impulse response (CIR) data at each tag position on the path. The resulting dataset supports the development of range-based positioning and position tracking algorithms with various combinations of network topologies and anchor-tag combinations. We have also provided a full set of data analysis tools that enable the reproducibility of results and serve as a basis for further development of range-based UWB positioning algorithms.

A Clustering Technique for Ultrawideband Channel Using Modified Affinity Propagation

In this study, we present an ultra-wideband clustering algorithm using affinity propagation (AP). Unlike typical AP algorithms, which define similarity as a negative squared Euclidean distance between data points, we define it to reflect the amplitude discontinuity observed in Saleh-Valenzuela models. The proposed algorithm was tested on simulated channel impulse response and propagation data measured in an indoor environment, and reasonable clustering results were observed.

Deep Learning based Wireless Channel Prediction: 5G Scenario

In the area of wireless communication, channel estimation is a challenging problem due to the need for real-time implementation as well as system dependence on the estimation accuracy. This work presents a Long-Short Term Memory (LSTM) based deep learning (DL) approach for the prediction of channel response in real-time and real-world non-stationary channels. The model uses the pre-defined history of channel impulse response (CIR) data along with two other features viz. transmitter-receiver update distance and root-mean-square delay spread values which are also changing in time with the channel impulse response. The objective is to obtain an approximate estimate of CIRs using prediction through the DL model as compared to conventional methods. For training the model, a sample dataset is generated through the open-source channel simulation software NYUSIM which realizes samples of CIRs for measurement-based channel models based on various multipath channel parameters. From the model test results, it is found that the proposed DL approach provides a viable lightweight solution for channel prediction in wireless communication.

A modern metric for acoustic clarity in critical listening environments

Characteristics of acoustics in small rooms have become less mysterious, more predictable and measurements more common than in past decades. Modern metrics for characterizing the acoustical performance of critical listening environments have not followed the recent predictive improvements. An Acoustic Clarity metric with a variable time window (Rear Reflection Clarity, CRR) is proposed for small (under approximately 200m3) critical listening environments such as recording studio control, mastering, film post, screening, audiophile listening, and similar rooms. The variable time window is defined by the arrival time difference between direct sound and the reflection from the rear wall, which is typically the strongest reflection and is opposite the on-axis response of the loudspeakers. Measured impulse response data collected in situ is presented and analyzed as case studies to characterize CRR. For simplicity, this presentation will focus on only the stereo components of surround-sound environments.

UWB LOS/NLOS identification in multiple indoor environments using deep learning methods

Indoor location-aware service is booming in daily life and business activities, making the demand for precise indoor positioning systems thrive. The identification between line-of-sight (LOS) and non-line-of-sight (NLOS) is critical for wireless indoor time-of-arrival-based localization methods. Ultra-Wide-Band (UWB) is considered low cost among the many wireless positioning systems. It can resolve multi-path and have high penetration ability. This contribution addresses UWB NLOS/LOS identification problem in multiple environments. We propose a LOS/NLOS identification method using Convolutional Neural Network parallel with Gate Recurrent Unit, named Indoor NLOS/LOS identification Neural Network. The Convolutional Neural Network extracts spatial features of UWB channel impulse response data. While the Gate Recurrent Unit is an effective approach for designing deep recurrent neural networks which can extract temporal features. By integrating squeeze-and-extraction blocks into these architectures we can assign weights on channel-wise features. We simulated UWB channel impulse response signals in residential, office, and industrial scenarios based on the IEEE 802.15.4a channel model report. The presented network was tested in simulation scenarios and an open-source real-time measured dataset. Our method can solve NLOS identification problems for multiple indoor environments. Thus more versatile compare with networks only working in one scenario. Popular machine learning methods and deep learning methods are compared against our method. The test results show that the proposed network outperforms benchmark methods in simulation datasets and real-time measured datasets.

A self-referencing non-destructive test method to detect damage in reinforced concrete bridge decks using nonlinear vibration response characteristics

Several non-destructive test (NDT) methods, namely visual inspection, hammer sounding, chain drag, impulse response testing, impact echo testing, ultrasonic (array) echo testing, and under certain conditions ground penetrating radar (GPR) are currently used to detect and estimate the extent of damage such as delaminations in reinforced concrete bridge decks. In this article, we present a self-referencing NDT method that builds on impulse response (IR) testing to detect damage using nonlinear vibration characteristics. The hypothesis is that for an undamaged deck, varying the impact force applied to a specific test point does not affect the corresponding frequency response function (FRF) for frequencies that lie within the measurement system’s linear operating range. On the other hand, the FRF for a test point that contains damage changes when the impact force is increased, indicating a nonlinear vibration response. To demonstrate that the concept works theoretically, two 2D finite element (FE) models of a bridge deck, one containing a shallow delamination, were developed and their responses to impact forces of increasing amplitude compared. IR data from an in-service bridge deck was processed and analyzed. Visual inspection results and ultra-high-pressure hydro-blasting performed on the deck for rehabilitation purposes provided an opportunity to compare the obtained results with common inspection methods and actual damage extent. Based on the observations, a new damage index, referred to as nonlinear vibration index (NVI), is proposed and shown to be sensitive to damage, including shallow delaminations that were missed by means of visual inspection.

Acoustic effects of face masks on speech: Impulse response measurements between two binaural mannikins

The COVID-19 pandemic has resulted in widespread use of facial masks that cover the nose and mouth areas to prevent viral transmission. While masks have helped provide protection from spreading illness, they have also had an impact on how people communicate, with many finding it more difficult to understand speech from those who are wearing masks. Facial masks affect the directivity and sound levels of the wearer’s verbal communication, and if opaque, they also cover visual cues that aid understanding. Studies have documented the effect of different mask types on speech intelligibility, speech transmission index, talker directivity, and resulting speech features. This talk presents the first of measurements towards composing a corpus of impulse responses gathered between two head and torso simulators (HATS) in an anechoic chamber at varying directional positions and distances. One HATS with a mouth simulator serves as the talker and is outfitted with different mask types, including cloth, N95, and surgical paper masks, in combination with face shields. The resulting impulse response data base may be used by researchers to simulate masked verbal communication scenarios such as in virtual soundfield environments, allowing systematic testing of how masks impact speech perception in numerous situations.

A performance comparison between m-sequences and linear frequency-modulated sweeps for the estimation of travel-time with a moving source.

This manuscript discusses the utility of maximal period linear binary pseudorandom sequences [also referred to as m-sequences or maximum length sequences (MLSs)] and linear frequency-modulated (LFM) sweeps for the purpose of measuring travel-time in ocean-acoustic experiments involving moving sources. Signal design and waveform response to unknown Doppler (waveform dilation or scale factor) are reviewed. For this two-parameter estimation problem, the well-known wide-band ambiguity function indicates, and moving-source observations corroborate, a significant performance benefit from using MLS over LFM waveforms of similar time duration and bandwidth. The comparison is illustrated with a typical experimental setup of a source suspended aft of the R/V Sally Ride to a depth of∼10 m and towed at∼1 m/s speed. Accounting for constant source motion, the root mean square travel-time variability over a 30 min observation interval is 53 μs (MLS) and 141 μs (LFM). For these high signal-to-noise ratio channel impulse response data, LFM arrival-time fluctuations mostly appear random while MLS results exhibit structure believed to be consistent with source (i.e., towed transducer) dynamics. We conclude with a discussion on signal coherence with integration times up to 11 MLS waveform periods corresponding to ∼27 s.

Defect detection in concrete plates with impulse-response test and statistical pattern recognition

Statistical and machine learning analysis of structural health monitoring data is a popular approach for efficient structural level defect detection. However, the application of these techniques to stress-wave methods such as impact-echo and impulse-response data for local level detection has been limited. In this research, statistical pattern recognition in conjunction with the impulse-response test is shown to provide an efficient means for the detection of defects in concrete plates. For this purpose, the Frequency Response Function (FRF) derived from the impulse-response test following ASTM C1740 protocols at specific points on the test plate to define the feature space matrix. Analytical results demonstrate that the variability of the FRF increases in the presence of defects, which forms the physical basis for the proposed pattern recognition algorithm. First, Principal Component Analysis (PCA) is performed on the covariance of the feature space matrix to identify the dominant features of the FRFs and to determine the number of statistically significant factors or principal components. Factor scores are next used to identify locations on the plate that are associated most closely to each pattern. The generalized Extreme Value Studentized (ESD) test and box and whisker plots are applied to the factor score vector of all the test points to objectively identify test points with defects and rank these based on their severity. Two experimental specimens are used to demonstrate the applicability of the proposed detection algorithm. The first specimen, a partially reinforced clamped concrete plate, is used to demonstrate the relationship between the shapes and variability of the FRFs and the severity of defects (delaminations), the efficacy of the factor score as damage sensitive feature, and identification and the ranking of the severity of defects by using outlier statistical tests. The second specimen is used to demonstrate the efficiency of the procedure for detecting both void and honeycomb defects in a larger reinforced concrete plate on elastic supports. The proposed procedure is shown to provide similar levels of detectability as the highly refined yet time consuming ultrasonic shear-wave tomography test. While the impulse-response test has been in use for the condition assessment of concrete elements other than drilled shaft piles since 1980′s, defect detection has been based primarily on empirical observations and correlations with limited features of the frequency response function. The use of pattern recognition techniques to the full range of the frequency response function is proposed and shown to greatly improve the detection and characterization of defects.

Error model-based iterative compensation method for improving the estimation of frequency response function

For the estimation of frequency response function (FRF) using the window-based spectral estimation method, the error models of the FRF estimation are introduced, including window interpolation error and transient error models, and an error model-based iterative compensation (EMIC) method is then proposed to improve the FRF estimation accuracy. The specific iteration steps of the proposed EMIC method are presented, fast computations are adopted in the steps as far as possible; and that, iteration convergence and noise uncertainty are analyzed. The analyses reveal that, the proposed EMIC method is almost always convergent as long as the error of the FRF pre-estimation is small, intercepting enough data and using a window which has smaller and fewer side DFT bins as well as smaller front-end value for FRF estimation can result in better iteration convergence; whereas, the proposed EMIC method will slightly increase the noise uncertainty of FRF estimation compared with that without iterative compensation because it removes the window interpolation effects on noise just like removing the error. To verify the effectiveness and practicability of the proposed EMIC method, model simulation and actual sensor data verification are implemented, and multi existing methods are employed for comparison. The results show that, the proposed EMIC method can largely decrease the error of the FRF estimated by the window-based spectral estimation method, it has the best maximum error restraining capabilities especially in the resonance frequency band and relatively better comprehensive error restraining capabilities especially when the data is not long enough compared with the mentioned existing methods, also, the time consumption of the proposed EMIC method is acceptable and is usually seconds or less; and that, the theoretical analyses, model simulation results and actual data verification results are consistent with each other. Consequently, the proposed EMIC method is proven to be effective and practical for improving the FRF estimation of a stable LTI system from its step or impulse response data.

Output-Only Modal Estimation Using Eigensystem Realization Algorithm with Nonstationary Data Correlation

The conventional eigensystem realization algorithm with data correlation (ERA/DC) combines the impulse response or free response data of a structural system with the concept of correlation function to identify the modal parameter of the structural system. Previous studies have shown that the modal parameters of structural systems subjected to stationary white noise excitation can be estimated by ERA/DC from the ambient response without excitation data. This concept is extended in this paper for output-only modal identification for the structural system with complex modes under ambient excitation as a nonstationary process in the form of a product model. Numerical simulations and experimental verification are used to validate the effectiveness of the proposed method for response-only modal estimation, and the stabilization diagram is used with modal assurance criterion (MAC) to distinguish structural modes from fictitious modes.

Locating Image Sources from Multiple Spatial Room Impulse Responses

Measured spatial room impulse responses have been used to compare acoustic spaces. One way to analyze and render such responses is to apply parametric methods, yet those methods have been bound to single measurement locations. This paper introduces a method that locates image sources from spatial room impulse responses measured at multiple source and receiver positions. The method aligns the measurements to a common coordinate frame and groups stable direction-of-arrival estimates to find image source positions. The performance of the method is validated with three case studies—one small room and two concert halls. The studies show that the method is able to locate the most prominent image sources even in complex spaces, providing new insights into available Spatial Room Impulse Response (SRIR) data and a starting point for six degrees of freedom (6DoF) acoustic rendering.

Piezoelectric mirror shifter transfer function measurement, modelling, and analysis using feedback based synthetic-heterodyne Michelson interferometry

Laser vibrometry has many applications in non-contact dynamic displacement and vibration measurement. A test beam reflected from a target and a reference beam are combined and detected by a photodiode; the photodetected signal is then processed to determine the target displacement and vibration. This paper describes the use of a 9 kHz measurement bandwidth system, consisting of a Michelson interferometer and self-correcting feedback synthetic-heterodyne signal processing technique, to measure the displacement impulse response of a commercial piezoelectric mirror shifter (PMS), consisting of a mirror mounted on a Piezoelectric transducer and a connecting 50 Ω electrical coaxial cable. The actual non-ideal applied impulse and measured impulse response data were used in conjunction with the instrument variable method to determine a Laplace domain linear transfer function approximation to the actual PMS transfer function. The best transfer function fitting, having a 84% normalized root mean square goodness of fit, was obtained using a 5-th order transfer function having two complex conjugate pole pairs, with associated natural frequencies of 6.29 and 6.79 kHz, and a single real pole. The transfer function zeros consisted of a single complex conjugate zero pair, having an antiresonance frequency of 6.38 kHz and a single real zero. Knowing the analytic transfer function of PMS based nanopositioners is useful for example in the design of closed-loop phase-locked interferometers for wideband sensing.

Highly Spectral Efficient C + L-Band Transmission Over a 38-Core-3-Mode Fiber

High capacity transmission with a data rate over 10.66 Pb/s is demonstrated in a 13 km, 38-core-3-mode few-mode multi-core fiber. This was achieved with a single optical comb source to generate 368 × 24.5 GBaud 64- or 256-quadrature-amplitude-modulated signals in a 25 GHz wavelength channel grid in combination with a few-mode multi-core fiber with high uniformity amongst cores. We investigated essential transmission channel characteristics and found that mode-dependent loss strongly decreases the achievable data rates in some cores, while we did not observe a correlation between varying impulse response spread and data rate. We further did not find an influence of the (radial) position of a core within the cladding on the data rate. Finally, we explored the potential of multi-span transmission over the same fiber with transmission distances up to 65 km, showing a strong performance penalty from prototype components with sub-optimal transmission properties.

Acoustic Performance from a Mixture of Plastic Waste, Wood Dust, and Rice Husk

Indonesia is a country with the second-largest amount of plastic waste into sea pollution in the world. The volume of Indonesia's plastic waste imports in 2018 reached 320 thousand tons. To reduce plastic waste, the processing innovations need to be done, and one of them is for building material in the form of a brick. This study used a mixture of plastic waste, sawdust, and rice husk with cement adhesive through a specific ratio. The research aims to test the ability of concrete blocks to absorb sound. The research method used was the test of sound absorption ability by 20 samples of brick making test material. The sound source was generated from the shotgun. Then the sound recorded by the dodecahedron microphone which was connected to Adobe Audacity software. Processing impulse response data used the Real-Time Analyzer program. The concrete test material was made from a mixture of plastic, sawdust and rice husk in a ratio of 30/70, 50/50, and 40/60. The sound absorption test was carried out in an enclosed 3.5x3.5 m2 acoustic laboratory. The concrete block, which was a mixture of plastic and rice husk with a ratio of 30/70, was the best material for absorbing sound. The sound could be absorbed up to 1.2 decibels, while the mixture of plastic and rice husk with a ratio of 60/40 was the lowest material in the ability to absorb sound. High-capacity concrete blocks could not only absorb the noise but also be used as building space separating materials.