Acoustic Impulse Response Research Articles

Insertable, dual-density dielectric barrier for acoustic pressure level reduction in a high-performance human head-only MRI system

We report use of a dual-density dielectric barrier surrounding a detachable high-pass radiofrequency (RF) birdcage coil to achieve an order-of-magnitude reduction of acoustic noise in a high-performance head gradient system. The barrier consisted of a 4.5 mm-thick mass-loaded vinyl and a 6 mm-thick polyurethane foam. It was inserted into the radial gap between the birdcage coil and the RF shield in a prototype head-only gradient system at 3 T. More than 9 dBA reduction of sound pressure level was achieved on the average with representative, high acoustic-noise imaging sequences. Increased acoustic damping was apparent from acoustic impulse response functions. High dielectric constant of the mass-loaded vinyl effectively added distributed capacitance to the birdcage coil, lowering the resonance frequency, but not seriously degrading the RF transmission performance. The barrier occupied the radial space normally used for air cooling of the RF coil and the RF shield. The resulting omission of air cooling was found to be acceptable with efficient gradient thermal management and use of a high-resistivity RF shield for eddy current reduction. The proposed method can improve patient experience while preserving image quality in a high-power head-only gradient system.

Doppler frequency analysis for sound-field sampling with moving microphones

Moving microphones allow for the fast acquisition of sound-field data that encode acoustic impulse responses in time-invariant environments. Corresponding decoding algorithms use the knowledge of instantaneous microphone positions for relating the dynamic samples to a positional context and solving the involved spatio-temporal channel estimation problem subject to the particular parameterization model. Usually, the resulting parameter estimates are supposed to remain widely unaffected by the Doppler effect despite the continuously moving sensor. However, this assumption raises issues from the physical point of view. So far, mathematical investigations into the actual meaning of the Doppler effect for such dynamic sampling procedures have been barely provided. Therefore, in this paper, we propose a new generic concept for the dynamic sampling model, introducing a channel representation that is explicitly based on the instantaneous Doppler shift according to the microphone trajectory. Within this model, it can be clearly seen that exact trajectory tracking implies exact Doppler-shift rendering and, thus, enables unbiased parameter recovery. Further, we investigate the impact of non-perfect trajectory data and the resulting Doppler-shift mismatches. Also, we derive a general analysis scheme that decomposes the microphone signal along with the encoded parameters into particular subbands of Doppler-shifted frequency components. Finally, for periodic excitation, we exactly characterize the Doppler-shift influences in the sampled signal by convolution operations in the frequency domain with trajectory-dependent filters.

Acoustic echo cancellation based on two‐stage BLSTM

AbstractAcoustic echo cancellation (AEC) methods aim to suppress the acoustic coupling for hands‐free speech communication. Traditional AEC works by identifying the acoustic impulse response using adaptive algorithms. With recent research advances, deep learning has become an attractive choice for AEC. This paper introduces a two‐stage bidirectional long short term memory (TS‐BLSTM) framework, incorporating multi‐head self‐attention mechanisms after each BLSTM block. This is aimed at better capturing contextual information and further enhancing ability of the model to handle complex acoustic scenarios. The BLSTM blocks are utilized to aggregate magnitude spectrum information, modelling both time and frequency dependencies. Additionally, dilation convolution is introduced to broaden the range of information in each convolution output. The magnitude decoder estimates a mask for the input, resulting in the generation of an estimated magnitude spectrum for near‐end speech. Experimental results indicate that the proposed method achieves promising outcomes.

Micro-multipath interference and its effect on sub-minute acoustic fluctuations: Observations of mid-frequency acoustic propagation in the upper ocean

An experiment was conducted to investigate how variability in the acoustic channel impulse response at signal processing timescales of seconds to minutes is related to internal-wave-driven changes in the ocean sound speed field on timescales of tens of minutes. We find that vertical fine-scale structures (≤10 m) embedded in the sound speed profile have strong second derivatives which allow for the formation of acoustic caustics as well as acoustic propagation pathways that interfere with each other (micro-multipaths). Both effects are sensitive to vertical displacement by internal waves, which can therefore produce sub-minute acoustic fluctuations from internal waves that have timescales longer than a minute.

Model based spectral feature analysis in reverberant acoustic fields

The spectral features of a reverberant acoustic field that can lead to improved source localization methods are investigated. Of particular interest are identification of deterministic and probabilistic features that characterize the environment and models of the acoustic transfer function that generate such features. For example, models that predict Schroeder’s invariant standard deviation in the pressure response at source-receiver distances where the direct sound approaches the reverberant field energy are proposed. Models examined include non-stationary Gaussian processes for the acoustic reverberant components and all-pass parametric models of the acoustic impulse response.

Specifications of bioinspired material structural foam prosoma of Tachpleus gigas and Carcinocorpius rotundicaud using acoustic impulse response

Renewable material innovation has shifted from inorganic materials to biological materials. Especially due to the demands of modern applications in food, biomedicine, and environmental sustainability. The peculiarity of the horseshoe crab is in the prosoma with a hollow structure (foam). The development of structural specifications also leads to non-destructive modern measurements using acoustic response impulse. This study aimed to determine the specifications foam material (shape and size) of the prosoma Tachypleus gigas and Carcinoscorpius rotundicauda using acoustic impulse responses. The research includes the characterization of the components and structure of the foam material prosoma using acoustic impulse response The prosoma components of T. gigas and C. rotundicauda have high protein and crude fiber, as well as functional groups according to chitin. The weight of T. gigas and C. rotundicauda ranges 484.33 ± 5.13 g and 493.33 ±15.28 g and densities of 1206.45 ± 148.01 kg/m3 and 1264.60 ± 120.01 kg/m3, but have differences in pore diameter and strut, 431.39μm ± 166.22 and 153.30μm ± 33.62 for pore diameter and strut, 120.09μm ± 57.83 and 98.0881μm ± 26.75 and compressive strength of 220.68 kPa and 285.88 kPa, and elasticity of 17.85 and 38.12.

Dimensionality Reduction of Room Acoustic Impulse Responses and Applications to System Identification

Dimensionality Reduction of Room Acoustic Impulse Responses and Applications to System Identification

Study of acoustic propagation across an oceanic front at the edge of the New England shelf.

Propagation of low-frequency sound across a warm core ring-enhanced oceanic front at lower horizontal grazing angles is presented. The data were collected from an experimental source tow conducted during the New England Shelf Break Acoustics (NESBA) experiments in spring 2021. The 3 h tow track provides spatiotemporal measurements of acoustic propagation through the front across varying geometries. Coincident oceanographic measurements are used to estimate the strong temperature gradient of the water column and three-dimensional (3D) sound speed field. Two-dimensional (2D) adiabatic mode and full-field sound propagation models are utilized to investigate the acoustic sensitivity to the frontal structure. Then, the joint effects of acoustic ducting and bathymetric slope refraction are examined using 3D sound propagation models. Key components of the measured acoustic impulse response are captured in the 3D numerical model, and the sensitivity of low-frequency propagation to the front geometry is demonstrated.

Non-destructive detection of codling moth infestation in apples using acoustic impulse response signals

Codling moth (CM) ( Cydia pomonella L.) is the most destructive pest for apples, causing large economic losses when not properly mitigated. Efficient detection methods can limit the spread of this pest in the apple supply chain. Non-destructive methods have several advantages over the current methods in that they can be applied to every apple (or a much larger sample) thereby reducing the possibility of missed detection. This paper examines the feasibility of acoustic impulse response methods for detecting CM larvae-infested apples. Experiments were performed on control and artificially infested apples from three different cultivars. Signals were recorded with a contact sensor, and 21 signal features were proposed and extracted to characterise relevant properties of the response. The 21 features were evaluated with 11 machine leaning algorithms to determine if the features or their subsets contained information that could reliability determine if an apple was/is infested. Classification test results using a 10-fold cross-validation indicated accuracy rates between 80% and 92% for Fuji apples, between 92% and 99% for Gala apples, and 63% and 97% for Granny Smith apples. The impulse response required between 60 and 80 ms for each apple (not counting setup/transition time). These results from this study suggest that active impulse response classification can potentially improve the detection of post-harvest apple CM infestation detection along the supply chain. • Acoustic impulse response signals were investigated for detection of codling moth in apples. • Ultrasonic sound from solenoid tapping were attenuated differently based on apple quality. • Ensemble methods were the better classifiers for differentiating signals from apples. • Classification rates for Gala were the best ranging between 0.92 and 0.99.

REPRESENTATIVE OF THE STATE OF REVERBERATION IN THE SERBIAN ORTHODOX CHURCH

In this paper, the statistical analysis determined a representative of the state of reverberation in theSerbian Orthodox Church of "St. Prokopius" in Katun. The first part of the paper explains the experiment based onthe process of recording acoustic impulse responses and determines the value of the early reverberation time EDTand the reverberation time RT10, RT20, and RT30 at the central frequencies fc = {125, 250, 500, 1000, 2000, 4000} Hzusing ARTA software package. The experiment results (tabular and graphical) obtained by statistical analysis usingthe Matlab software package are also presented. In the second part of the paper, the results of the experimentanalysis were carried out. By comparing them, it was concluded which acoustic parameter is representative of thestate of reverberation in the analyses of the Serbian Orthodox church.

Investigation of the Physical Mechanism of Acoustic Attenuation in Viscous Isotropic Solids

The traditional acoustic attenuation coefficient is derived from an analogy of the attenuation of an electromagnetic wave propagating inside a non-ideal medium, featuring only the attenuation of wave propagation. Nonetheless, the particles inside viscous solids have mass, vibrating energy, viscosity, and inertia of motion, and they go through transient and damping attenuation processes. Based on the long-wavelength approximation, in this paper, we use the energy conservation law to analyze the effect of the viscosity of the medium on acoustic attenuation. We derive the acoustic attenuation coefficient by combinations of the dynamical equation of a solid in an acoustic field with conventional longitudinal wave propagation under a spring oscillator model. Considering the attenuation of propagating waves and the damping attenuation of particle vibration, we develop a frequency dispersion relation of phase velocity for the longitudinal wave propagating inside viscous solid media. We find that the acoustic impulse response and vibrational system function depends on the physical properties of the viscous solid media and their internal structure. Combined with system function, the impulse response can be an excellent tool to invert the physical properties of solids and their internal structures. We select a well-known rock sample for analysis, calculate the impulse response and vibrational system function, and reveal new physical insight into creating acoustic attenuation and frequency dispersion of phase velocity. The results showed that the newly developed acoustic attenuation coefficients enjoy a substantial improvement over the conventional acoustic attenuation coefficients reported in the literature, which is essential for industrial applications; so are the dispersion characteristics.

Supervised speech separation combined with adaptive beamforming

Microphone arrays are a powerful tool for ambient noise suppression. A multi-channel minimum mean square error (MMSE) solution can be factorized into a minimum variance distortionless response beamformer (MVDR) followed by a single-channel Wiener post-filter. MVDR beamformer, as well as its equivalent form of generalized sidelobe canceller (GSC), often does not provide sufficient noise reduction due to its limited ability to reduce diffuse noise and reverberation. Steering and calibration errors also degrade the performance of both MVDR and GSC beamformers. Post-filter can be realized by any single-channel noise reduction method. A modern and promising approach for single-channel noise reduction is formulated as a supervised speech separation (SSS) in which a supervised learning algorithm, typically a deep neural network (DNN), is trained to learn a mapping from the noisy features to a time-frequency representation of the target of interest. In this paper, we combined SSS and adaptive beamforming approaches. Adaptive beamforming is realized by simplified GSC (S-GSC) whose equivalence with MVDR beamformer is also proved in the paper. In the proposed S-GSC beamformer, the conventional beamformer is replaced by the central microphone signal. Steering towards the target speaker needs no direction of arrival (DOA) estimation. Trained DNN of the SSS module estimates ideal ratio mask (IRM) which is used for adaptation of the blocking matrix, calibration of the microphones, adaptation for the adaptive noise canceller, and the post-filtering. The proposed method was tested on 720 utterances of the TIMIT database used as target speech. The reverberant room was simulated by acoustic impulse responses recorded in the real room. Performance analysis was carried out with PESQ, STOI, and SDR measures. The test results showed that the proposed combined method outperforms the individual SSS and S-GSC methods.

Controlling the Spatiotemporal Response of Transient Reverberating Sound

Sound propagating inside a room is multiply scattered by the boundaries and other obstacles, forming a complex reverberating sound field (RSF). Such RSFs are not only spatially disordered but also temporally scrambled. Although the spatial control of steady-state RSFs at single frequencies is successfully achieved by extending the idea of wavefront shaping to acoustics, time-coherent polychromatic control of RSFs has remained out of reach. Here, we report spatiotemporal acoustic wavefield shaping by adaptively reshaping acoustic impulse responses at arbitrary positions and different instants in a reverberating room using spatial sound modulators---a reconfigurable and programmable membrane-type acoustic metasurface capable of encoding desirable phase factors over a finite bandwidth. We further analyze the mechanism and performance of spatiotemporal wavefield shaping with a simple numerical model derived from multiple scattering in the time domain. Our results expand the capabilities of RSF control to the temporal domain and demonstrate the possibilities for transient engineering of polychromatic sound in acoustic communication, imaging, and holography.

Online Acoustic System Identification Exploiting Kalman Filtering and an Adaptive Impulse Response Subspace Model

We introduce a novel algorithm for online estimation of Acoustic Impulse Responses (AIRs) which allows for fast convergence by exploiting prior knowledge about the fundamental structure of AIRs. The proposed method assumes that the variability of AIRs of an acoustic scene is confined to a low-dimensional manifold which is embedded in a high-dimensional space of possible AIR estimates. We discuss various approaches which exploit a training data set of AIRs, e.g., high-accuracy AIR estimates from the acoustic scene, to learn a local affine subspace approximation of the AIR manifold. The model is motivated by the idea of describing the generally nonlinear AIR manifold locally by tangential hyperplanes and its validity is verified for simulated data. Subsequently, we describe how the manifold assumption can be used to enhance online AIR estimates by projecting them onto an adaptively estimated subspace. Motivated by the assumption of manifolds being locally Euclidean, the parameters determining the adaptive subspace are learned from the nearest neighbor AIR training samples to the current AIR estimate. To assess the proximity of training data AIRs to the current AIR estimate, we introduce a probabilistic extension of the Euclidean distance which improves the performance for applications with non-white excitation signals. Furthermore, we describe how model imperfections can be tackled by a soft projection of the AIR estimates. The proposed algorithm exhibits significantly faster convergence properties in comparison to a high-performance state-of-the-art algorithm. Furthermore, we show an improved steady-state performance for speech-excited system identification scenarios suffering from high-level interfering noise and nonunique solutions.

Identification of Room Acoustic Impulse Responses via Kronecker Product Decompositions

The identification of room acoustic impulse responses represents a challenging problem in the framework of many important applications related to the acoustic environment, like echo cancellation, noise reduction, and microphone arrays, among others. In this context, the main issues are related to the long length of such impulse responses and their time-variant nature. These raise significant difficulties in terms of the convergence rate, computational complexity, and accuracy of the solution. Recently, a decomposition-based approach was developed for the identification of low-rank systems, which can also be applied (to some extent) for the identification of acoustic impulse responses. This approach exploits the nearest Kronecker product decomposition of the impulse response and solves a high-dimension system identification problem using a combination of low-dimension solutions (provided by shorter filters), thus gaining in terms of both performance and complexity. Nevertheless, it does not consider the intrinsic nature of the room acoustic impulse responses, which contain specific components (e.g., early reflections and late reverberation) that can be very different in nature. In this paper, we propose an improved decomposition-based method (via the Kronecker product) that takes into account these specific components and processes them separately, in order to better exploit their important low-rank features. Following this approach, an iterative Wiener filter is firstly developed, followed by a recursive least-squares (RLS) algorithm designed in the same framework. Both solutions outperform the conventional benchmarks, i.e., the conventional Wiener filter and the RLS algorithm, respectively. Moreover, they achieve superior performances as compared to the recently developed versions based on the nearest Kronecker product decomposition, also owning lower computational complexities than their previous counterparts. Simulations are performed in the framework of acoustic echo cancellation and the obtained results support the performance features of the proposed algorithms.

Estimation of Acoustic Channel Impulse Response at Low Frequencies Using Sparse Bayesian Learning for Nonuniform Noise Power

Sparse Bayesian learning (SBL) has been extended to estimate acoustic channel impulse responses (CIRs) at low frequencies, where matched filter (MF)-based CIR estimation suffers from low resolution due to a limited frequency band. In this study, the extended SBL was developed to account for nonuniform noise power in a signal model for the CIR via a formulation that considers inconsistent noise and multiple measurements, which cannot be handled in the conventional SBL. The extended SBL is applied to simulated and measured acoustic data and then compared with the MF and existing SBLs. With the advancement in the schemes, the time resolution and denoising are enhanced; especially, the results of the extended SBL on the simulated data show that it clearly distinguishes the two adjacent arrivals with moderate errors in the estimated time delays. Additionally, as a result of applying it to the measured array data, the extended SBL can achieve a high resolution in the CIR estimation, which retains assured arrivals with a single measurement, while some arrivals are weakened by the insufficient measurement for the extended SBL.

Perceptual Matching of Room Acoustics for Auditory Augmented Reality in Small Rooms - Literature Review and Theoretical Framework.

For the realization of auditory augmented reality (AAR), it is important that the room acoustical properties of the virtual elements are perceived in agreement with the acoustics of the actual environment. This perceptual matching of room acoustics is the subject reviewed in this paper. Realizations of AAR that fulfill the listeners’ expectations were achieved based on pre-characterization of the room acoustics, for example, by measuring acoustic impulse responses or creating detailed room models for acoustic simulations. For future applications, the goal is to realize an online adaptation in (close to) real-time. Perfect physical matching is hard to achieve with these practical constraints. For this reason, an understanding of the essential psychoacoustic cues is of interest and will help to explore options for simplifications. This paper reviews a broad selection of previous studies and derives a theoretical framework to examine possibilities for psychoacoustical optimization of room acoustical matching.

Neural Cascade Architecture for Multi-Channel Acoustic Echo Suppression

Traditional acoustic echo cancellation (AEC) works by identifying an acoustic impulse response using adaptive algorithms. This paper proposes a neural cascade architecture for joint acoustic echo and noise suppression to address both single-channel and multi-channel AEC (MCAEC) problems. The proposed cascade architecture consists of two modules. A convolutional recurrent network (CRN) is employed in the first module for complex spectral mapping. Its output is fed as an additional input to the second module, where a long short-term memory network (LSTM) is utilized for magnitude mask estimation. The entire architecture is trained in an end-to-end manner with the two modules optimized jointly using a single loss function. The final output is generated using the enhanced phase and magnitude obtained from the first and the second module, respectively. The cascade architecture enables the proposed method to obtain robust magnitude estimation as well as phase enhancement. The proposed method is investigated under different AEC setups. We find that the deep learning based approach avoids the no-uniqueness problem in traditional MCAEC. For MCAEC setups with multiple microphones, combining deep MCAEC with supervised beamforming further improves the system performance. Evaluation results show that the proposed approach effectively suppresses acoustic echo and noise while preserving speech quality, and consistently outperforms related methods under different setups.

Low resolution fourier synthesis modelling for underwater acoustic channel impulse response

Fourier synthesis is the most widely used method for underwater acoustic broadband modeling. Traditionally, high frequency resolution and large time window length are needed to generate broadband time series to avoid aliasing in time domain. An optimized Fourier synthesis method is presented in this paper to accelerate the impulse response function modelling. The method consists of two steps, the phase-frequency response reconstruction and the impulse response function time window determination. The impulse response function of a range-dependent ocean channel is modeled using the proposed method with different frequency resolutions and compared with the interpolation method and traditional Fourier synthesis method. Modeling results show that the proposed method requires only a tenth of the frequency resolution of the traditional Fourier synthesis method and the correlation coefficients of modelling output time series are preserved above 95%. The proposed method also exhibits better phase response reconstruction performances than the interpolation method when the frequency resolution equals to 1 Hz.

Designing, manufacturing, and evaluating the diagnostic system of carob moth in pomegranate fruit using digital signal processing

Sound analysis is one of the least destructive testing methods that have been developed for the determination of fruit quality. Therefore, in this research, a system was developed for analyzing the acoustic impulse response to determine the presence of carob moths in pomegranate fruit. For this purpose, acoustic signals generated from 100 pomegranate fruits were recorded (50 healthy and 50 infected). In order to record and extract the acoustic signals' characteristics (non-destructive test), a plastic pendulum hit the samples at three speeds (0.3, 0.9, and 1.5 m/s) and a microphone was located at three positions concerning impactor (0, 90, and 180°). For data classification, Fisher's linear discriminant classifier combined with three different feature selection methods was used. According to the statistical analysis, a speed of 0.9 m/s was selected as the optimum impact velocity. According to the classification results, the best features were natural frequency, spectral entropy, and zero-crossing, which had the least classification error. The application of these three features resulted in a classification accuracy of 97.55%.