Acoustic Model Research Articles

Enhanced broadband sound absorption of multi-layer composite material based on three-dimensional warp-knitted spacer fabric

Compared with other sound-absorbing materials, spacer fabric with a “sandwich” structure has many advantages, such as light weight, an environmentally friendly production process, recyclability, versatility, low production cost and high efficiency, and strong designability of the organizational structure. However, as a porous sound-absorbing material, the effective sound-absorbing frequency band of the spacer fabric is concentrated at high frequencies, so the sound-absorbing performance needs to be improved. This paper establishes a Johnson–Champoux–Allard theoretical model by using a genetic algorithm to characterize the sound absorption performance of the three-dimensional (3D) warp-knitted spacer fabric. The theoretical model is validated through finite element analysis and experimental methods. Based on this model, two composite materials are designed to improve the sound absorption performance: (1) multi-layer spacer fabric composite material and (2) multi-layer spacer fabric-aluminum foil composite material. Numerical simulation and experimental results show that the sound absorption performance is significantly improved by widening the effective frequency band and increasing the sound absorption coefficient. The contribution of this paper is twofold. Firstly, the theoretical acoustic model of 3D warp-knitted spacer fabric is established. Secondly, the superior sound absorption performance is achieved by proposing multi-layer and multi-material composite material. The spacer fabric-based composite material has broad application prospects in the field of noise reduction and sound insulation. The paper provides a theoretical basis and more strategies for the acoustic and multifunctional design of spacer fabric in the future.

A simulation study of transcranial magnetoacoustic stimulation of the basal ganglia thalamic neural network to improve pathological beta oscillations in Parkinson's disease

BackgroundParkinson's disease (PD) is a common neurodegenerative disease. Transcranial magnetoacoustic stimulation (TMAS) is a new therapy that combines a transcranial focused acoustic pressure field with a magnetic field to excite or inhibit neurons in targeted area, which suppresses the abnormally elevated beta band amplitude in PD states, with high spatial resolution and non-invasively. ObjectiveTo study the effective stimulation parameters of TMAS mononuclear and multinuclear stimulation for the treatment of PD with reduced beta band energy, improved abnormal synchronization, and no thermal damage. MethodsThe TMAS model is constructed based on the volunteer's computed tomography, 128 arrays of phase-controlled transducers, and permanent magnets. A basal ganglia-thalamic (BG-Th) neural network model of the PD state was constructed on the basis of the Izhikevich model and the acoustic model. An ultrasound stimulation neuron model is constructed based on the Hodgkin-Huxley model. Numerical simulations of transcranial focused acoustic pressure field, temperature field and induced electric field at single and dual targets were performed using the locations of STN, GPi, and GPe in the human brain as the main stimulation target areas. And the acoustic and electric parameters at the focus were extracted to stimulate mononuclear and multinuclear in the BG-Th neural network. ResultsWhen the stimulating effect of ultrasound is ignored, TMAS-STN simultaneously inhibits the beta-band amplitude of the GPi nucleus, whereas TMAS-GPi fails to simultaneously have an inhibitory effect on the STN. TMAS-STN&GPi can reduce the beta band amplitude. TMAS-STN&GPi&GPe suppressed the PD pathologic beta band amplitude of each nucleus to a greater extent. When considering the stimulatory effect of ultrasound, lower sound pressures of ultrasound do not affect the neuronal firing state, but higher sound pressures may promote or inhibit the stimulatory effect of induced currents. ConclusionsAt 9 T static magnetic field, 0.5–1.5 MPa and 1.5–2.0 MPa ultrasound had synergistic effects on individual STN and GPi neurons. TMAS multinuclear stimulation with appropriate ultrasound intensity was the most effective in suppressing the amplitude of pathological beta oscillations in PD and may be clinically useful.

Evaluation Method for Underwater Ultrasonic Energy Radiation Performance Based on the Spatial Distribution Characteristics of Acoustic Power.

In recent years, underwater wireless ultrasonic energy transmission technology (UWUET) has attracted much attention because it utilizes the propagation characteristics of ultrasound in water. Effectively evaluating the performance of underwater ultrasonic wireless energy transmission is a key issue in engineering design. The current approach to performance evaluation is usually based on the system energy transfer efficiency as the main criterion, but this criterion mainly considers the overall energy conversion efficiency between the transmitting end and the receiving end, without an in-depth analysis of the characteristics of the distribution of the underwater acoustic field and the energy loss that occurs during the propagation of acoustic waves. In addition, existing methods focusing on acoustic field analysis tend to concentrate on a single parameter, ignoring the dynamic distribution of acoustic energy in complex aquatic environments, as well as the effects of changes in the underwater environment on acoustic propagation, such as spatial variability in temperature and salinity. These limitations reduce the usefulness and accuracy of models in complex marine environments, which in turn reduces the efficiency of acoustic energy management and optimization. To solve these problems, this study proposes a method to evaluate the performance of underwater ultrasonic energy radiation based on the spatial distribution characteristics of acoustic power. By establishing an acoustic power distribution model in a complex impedance-density aqueous medium and combining numerical simulation and experimental validation, this paper explores the spatial variation of acoustic power and its impact on the energy transfer efficiency in depth. Using high-resolution spatial distribution data and actual environmental parameters, the method significantly improves the accuracy of the assessment and the adaptability of the model in complex underwater environments. The results show that, compared with the traditional method, this method performs better in terms of the accuracy of the acoustic energy radiation calculation results, and is able to reflect the energy distribution and spatial heterogeneity of the acoustic source more comprehensively, which provides an important theoretical basis and practical guidance for the optimal design and performance enhancement of the underwater ultrasonic wireless energy transmission system.

MF-Saudi: A multimodal framework for bridging the gap between audio and textual data for Saudi dialect detection

Detecting variations in dialects within a language can be challenging, particularly in regions with rich linguistic diversity like Saudi Arabia. To our knowledge, no prior attempts have been made to develop a multimodal, audio–textual framework for Saudi dialect detection. The current approaches often concentrate on detecting dialects only based on audio or textual data, which fails to capture the complex relationship between both modalities. In this paper, we propose a novel Multimodal Framework, called MF-Saudi, for Saudi dialect detection. The framework consists of three main components: (1) a pretrained BERT encoder for extracting and encoding textual information; (2) an acoustic model for representing audio signals and fusing them with textual information via the fusion layer; and (3) an alignment learning module to develop meaningful representations that capture the complexities of audio–text relationships, resulting in improved dialect detection. We conduct empirical evaluations on a real-world dataset, demonstrating that our solution outperforms some of the state-of-the-art baseline methods. The experiment’s code can be found here: https://github.com/raed19/MF-Saudi.

An analytical and numerical study of the Diaz–Solovchuk–Sheu acoustic model: How does it compare with Blackstock's in approximating the Euler system?

An analytical and numerical study of the Diaz–Solovchuk–Sheu acoustic model: How does it compare with Blackstock's in approximating the Euler system?

A rule-based framework for capturing geometric characteristics in design: A study of façade analysis for acoustic behaviour in urban space

In urban acoustics, the design of built environment substantially impacts the environmental noise of our everyday lives, as the built environment constantly reflects the sounds of human or non-human actors in its vicinity. The effects produced by these reflected sounds significantly depend on the geometric characteristics of the surrounding building façades. Despite their impact, the façade characteristics are often excluded from urban acoustic simulation models. As a result, sound reflection and scattering that are specifically related to building façades remain largely unexplored in noise control at the urban scale. Inspired by computational design tools used for exploring room acoustics, we investigate how a computational approach enables the analysis of geometric characteristics of façades to simulate urban acoustics during early design processes. This paper introduces a rule-based characterisation framework that enables the representation of geometric façade characteristics and their relationships with acoustic constraints in the form of rules. Our first results showed how these rules allowed for capturing and reconstructing façade geometries based on the given acoustic constraints. We believe that this novel approach empowers designers to describe their initial design ideas in a formalism that can be dynamically adapted to various dimensions, such as urban acoustics, and to reconstruct the form throughout the design process.

Strategies and safety simulations for ultrasonic cervical spinal cord neuromodulation

Objective. Focused ultrasound spinal cord neuromodulation has been demonstrated in small animals. However, most of the tested neuromodulatory exposures are similar in intensity and exposure duration to the reported small animal threshold for possible spinal cord damage. All efforts must be made to minimize the risk and assure the safety of potential human studies, while maximizing potential treatment efficacy. This requires an understanding of ultrasound propagation and heat deposition within the human spine. Approach. Combined acoustic and thermal modelling was used to assess the pressure and heat distributions produced by a 500 kHz source focused to the C5/C6 level via two approaches (a) the posterior acoustic window between vertebral posterior arches, and (b) the lateral intervertebral foramen from which the C6 spinal nerve exits. Pulse trains of fifty 0.1 s pulses (pulse repetition frequency: 0.33 Hz, free-field spatial peak pulse-averaged intensity: 10 W cm−2) were simulated for four subjects and for ±10 mm translational and ±10∘ rotational source positioning errors. Main results. Target pressures ranged between 20%–70% of free-field spatial peak pressures with the posterior approach, and 20%–100% with the lateral approach. When the posterior source was optimally positioned, peak spine heating values were below 1 ∘C, but source mispositioning resulted in bone heating up to 4 ∘C. Heating with the lateral approach did not exceed 2 ∘C within the mispositioning range. There were substantial inter-subject differences in target pressures and peak heating values. Target pressure varied three to four-fold between subjects, depending on approach, while peak heating varied approximately two-fold between subjects. This results in a nearly ten-fold range between subjects in the target pressure achieved per degree of maximum heating. Significance. This study highlights the utility of trans-spine ultrasound simulation software and need for precise source-anatomy positioning to assure the subject-specific safety and efficacy of focused ultrasound spinal cord therapies.

A PRISMA-driven Review of Speech Recognition based on English, Mandarin Chinese, Hindi and Urdu Language

Urdu Language ranks ten and is continuously progressing. This unique PRISMA-Driven review deeply investigates Urdu speech recognition literature and adjoin it with English, Mandarin Chinese, and Hindi languages frame-works conceptualizing wider global perspective. The main objective is to unify progress on classical Artificially Intelligent (AI) and recent Deep Neural Networks (DNN) based speech recognition pipeline encompassing Dataset challenges, Feature extraction methods, Experimental design and the smooth integration with both Acoustic models (AM) and Language models (LM) using Transcriptions. A total of 176 articles were extracted from Google Scholar database for each language with custom query design. Inclusion criteria and quality assessment leads to end up with 5 review and 42 research articles. Comparative research questions have been addressed and findings were organized by four possible speech types: Isolated, connected, continuous and spontaneous. The finding shows that English, Mandarin, and Hindi languages used spontaneous speech size of 300, 200 and 1108 hours respectively which is quite remarkable as compared to Urdu spontaneous speech data size of only 9.5 hours. For the same data size reason, the Word Error Rate (WER) for English falls below 5% while for Mandarin Chinese the alternative metric Character Error Rate (CER) is mostly used that lies below 25%. The success of English and Chinese Speech recognition leads to incomparable accuracy due to wide use of DNNs like Conformer, Transformers, E2E-attention in comparison to conventional feature extraction and AI models LSTM, TDNN, RNN, HMM, GMM-HMM; used frequently by both Hindi and Urdu.

A composite spread spectrum sequence for underwater acoustic signal acquisition

AbstractSpread spectrum technology has been widely employed for positioning and communicating with autonomous underwater vehicles (AUVs), but conventional spread spectrum sequences lack confidentiality and reliability in UWA channel. Considering the limitations of conventional sequences and the characteristics of underwater acoustic (UWA) channel, a composite chaotic orthogonal sequence (CCOS) based on the UWA channel is proposed. The confidentiality of the CCOS is superior to that of the m‐sequence, while the autocorrelation performance of the CCOS is superior to that of the orthogonal sequence. Moreover, the CCOS can compensate for the imbalance of logistic chaotic sequence when assigned certain initial values. Acquisition is a crucial component of accessing the spread spectrum signal; therefore, the acquisition performance indicates the applicability of the CCOS. The source–target model is established to simulate communication with an underwater moving target. To simulate the acquisition process, a parallel algorithm based on fast Fourier transform is adopted, and the entire simulation process is completed based on the BELLHOP ray acoustic model. Through data processing, the Doppler shift error is less than half of the frequency‐search element. Furthermore, the acquisition probabilities of the CCOS with different numbers of bits are over 90%, which demonstrates the reliability of the CCOS.

Research on noise prediction of steel aluminum mixed structure ship

Abstract In the context of the implementation of the new Ship Noise Level Regulations, it is particularly important to conduct ship noise prediction during the design phase. This article establishes a SEA model using the statistical energy method and VA One software and predicts cabin noise. The impact of non-living quarters, reinforced panels, and interior materials on noise prediction is discussed. The results indicate that using the statistical energy method for noise prediction has a certain degree of accuracy. When establishing an acoustic model, non-residential compartments can be simplified while considering the influence of reinforcement plates and interior materials on the prediction results.

Surface and underwater acoustic target recognition using only two hydrophones based on machine learning.

Surface and underwater (S/U) acoustic targets recognition is an important application of passive sonar. It is difficult to distinguish them due to the mixture of underwater target radiation noise and marine environmental noise. In previous studies, although using a single hydrophone was able to identify S/U acoustic targets, there were still a few hydrophones that had poor accuracy. In this paper, S/U acoustic targets recognition using two hydrophones based on Gradient Boosting Decision Tree is proposed, and it is first found out as high as 100% accuracy could be achieved with the implementation of SACLANT 1993 data. The real experimental data are always rare and insufficient. The big training dataset is generated using environmental information by acoustic model named KRAKEN. Simulation and experimental data used in the model are heterogeneous, and the differences between these two kinds of data are assimilated by using vertical linear array feature extraction method. The model realizes the recognition of S/U acoustic targets based on channel information besides source spectrum information. By using the combination of two hydrophones, the surface and underwater targets recognition accuracy reached 1 and 0.9384, while they are only 0.4715 and 0.5620 using a single hydrophone, respectively.

Active Control of Longitudinal Combustion Instability in Bluff-Body Stabilized Premixed Flames with Multiple Neural Network Controller

ABSTRACT The suppression performance of multiple neural network controller on the longitudinal combustion instability is investigated in the Volvo bluff-body stabilized premixed flame. Two controlled plant models, e.g. one-dimensional (1D) reduced order acoustic network model and three-dimensional (3D) large eddy simulation model are adopted to verify this control strategy. In order to build the reduced order Volvo acoustic network model, large eddy simulation is adopted to extract the characteristics of system heat release under wide bandwidth and construct the flame transfer functions. For the 1D acoustic network model, this controller can completely eliminate the pressure oscillation using fuel valve as actuator. For the 3D large eddy simulation model that mimics the real-time oscillating characteristics, clustered prediction method and amplitude limiting method are adopted in multiple neural network control strategy to better account for the nonlinear characteristics of the 3D Volvo flame. This control strategy enables the modulation of the phase relationship between the heat release rate oscillation and pressure oscillation by introducing small perturbations to the inlet equivalence ratio, and attenuates the amplitude of the second harmonic of the unstable mode effectively.

Unleashing geological sequestration potential of mature oilfield by enforcing 4-dimensional seismic model-based inversion in Widuri Field, Indonesia

Implementing reservoir characterization by undertaking seismic inversion on time-lapse surveys is very effective for observing the distribution and changes in the hydrocarbon reservoir. In mature oilfields, these changes are most likely influenced by the thermodynamic activities including the injection of fluid into the reservoir, which can change the volume, pressure, and composition of the geological formations. Fluid injection (in this case, water injection) into the reservoir is typically used as an oil and gas booster to increase production through EOR (Enhanced Oil Recovery). However, in light of CCUS (Carbon Capture Utilization and Storage) application, injecting CO2 can be considered as a new EOR strategy development, with the dual objectives of increasing oil and gas production as well as lowering carbon emissions at the same time. The 4-dimensional (4D) seismic data available over Widuri Field covers an area of 125 km2, with 884 inlines and 905 crosslines, acquired in 1991 and 2004 over the same area. The inversion algorithm is using seismic deconvolution to generate an acoustic impedance model before developing a geological reservoir model. Reservoir characterization was conducted in this study to obtain detailed information of the reservoir zones by determining the impact of water injection that replaces hydrocarbons. Intervals and areas with an abundance of water can be considered as potential CO2 sequestration in the future, as a part of the CCUS application. In conclusion, the findings of increasing impedance from inversion data from 1991 to 2004 can indicate the presence of existing porosity and permeability. This evidence could indicate reservoir capability as CO2 storage.

Performance analyses on the air cooling battery thermal management based on artificial neural networks

Air cooling thermal management technology is a lightweight and cost-effective approach for managing heat in power battery packs for electric ships. However, it suffers from significant drawbacks such as poor temperature control and excessive noise generation. To address these challenges, this study proposes a method that combines fluid dynamics and artificial neural networks (ANNs) to optimize the thermal management and aeroacoustic performance of the air cooling battery thermal management system (BTMS) for marine power batteries. Initially, a thermal-flow coupled model and an acoustic model for the BTMS were developed to investigate the impact of system structural parameters (battery spacing d, and main channel inclination angle θ) and operating parameter (system inlet velocity v) on the thermal management performance indicators (maximum temperature Tmax, and maximum temperature difference ΔTmax) as well as the aeroacoustic performance indicator (overall sound pressure level, OSPL). Subsequently, a relationship between the system structural and operating parameters and performance indicators was established using the 50-layer Residual Network (ResNet-50) model, enabling accurate and rapid prediction of the system thermal management and aeroacoustic performance. Furthermore, by combining ResNet-50 with the evaluation method of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), this study successfully obtained the optimal system structure and operating parameters. The research indicates that keeping the remaining parameters constant, increasing the battery spacing results in higher maximum temperature and maximum temperature difference, while decreasing the overall system sound pressure level. Conversely, increasing the main channel inclination angle results in lower maximum temperature and maximum temperature difference, but higher overall system sound pressure level. In addition, increasing the inlet velocity will result in higher maximum temperature and maximum temperature difference, as well as higher overall system sound pressure level. The optimal case were found to be a main channel inclination angle θ = 3°, battery spacing d = 2 mm, and inlet velocity v = 10 m⋅s−1. Compared to the base case, the optimal case shows a maximum temperature reduction of 10.63 K, a maximum temperature difference reduction of 9.41 K, and an overall sound pressure level reduction of 4.6 dB. The prediction errors for these values are 0.08 %, 2.64 %, and 1.36 % respectively. This research demonstrates an effective and rapid approach based on fluid dynamics and ANNs in the design and optimization of BTMS.

Evaluation of the physical characteristics of reinforced concrete subject to corrosion using a poro-elastic acoustic model inversion technique applied to ultrasonic measurements

The use of reinforced concrete is foundational to modern infrastructure. Acknowledging this, it is imperative that health monitoring techniques be in place to study corrosion within these structures. By using a non-destructive method for detecting the early formation of cracks within reinforced concrete, the method presented in this paper seeks to improve upon traditional techniques of monitoring corrosion, within reinforced concrete structures. In this paper, the authors present a method to evaluate the physical characteristics of reinforced concrete subject to corrosion using a poro-elastic acoustic model inversion technique applied to a set of ultrasonic measurements, which constitutes a novel approach to the problem of observing the impact of corroding rebars and resulting concrete damage. A non-contact ultrasonic transducer is operated at a carrier frequency of 500 [kHz], with a layer of saltwater separating the sensor from the concrete surface. Following this non-contact measurement collection of the surface and rebar echo responses, a poro-elastic model is used to model the sound propagation, through an adapted version of the Biot-Stoll model. At first, a set of default parameters, obtained from the physical characteristics of the reinforced concrete, are used to match experimental and simulated acoustic signature of the sample. Performing statistical averaging along the corroding rebar within three samples over a period of nearly nine months, a small but monotonous increase in the distance between the concrete surface and the top of the rebar, indicating gradual corrosion of the rebar. Next, a non-linear optimization algorithm is used to optimize the match between measured and simulated echoes. Through the implementation of this model parameter optimization, the root mean square error between measured and simulated responses was reduced by 63.7% for the full signal, and 62.6% for the rebar echo.

On the Cauchy problem for acoustic waves in hereditary fluids: Decay properties and inviscid limits

This paper considers the viscous/inviscid Moore–Gibson–Thompson (MGT) equations with memory of type I in the whole space . For one thing, associating with a new condition on initial data, we derive the optimal estimates and the optimal leading term of the acoustic velocity potential for large time, where we analyze different contributions from viscous, thermally relaxing, as well as hereditary fluids on large time asymptotic behavior for the acoustic waves models. For another, by using the multiscale analysis and energy methods in the Fourier space, we demonstrate the inviscid limits (i.e., as the diffusivity of sound tends to zero), which match our Wentzel‐Kramers‐Brillouin (WKB) expansion of the solution. Finally, we give a further application of our results on large time behavior for the nonlinear Jordan‐MGT equation in viscous hereditary fluids.

Spectral recomposition feature for optimizing seismic velocity model prediction with a neural network

An approach incorporating spectral recomposition results for training a neural network is proposed. The initial procedure is based on an inversion scheme for reconstructing the seismic spectrum of wavelets associated with a reflection event. This allows one to estimate each wavelet's temporal position within a seismogram. Since each calculated wavelet's temporal position is identified, this information is integrated into the seismogram as an additional feature for a trained neural network. A Fully Convolutional Network is trained with models that have features commonly found in offshore hydrocarbon reservoirs. The training step is based on composing a set of an acoustic velocity model set with its seismograms and their position matrices at the time of each wavelet for each seismogram. Next, the network training with a specific number of sets is performed. This approach was able to predict acoustic velocity models more accurately, especially for predicting the shape of salt bodies and the interface of inclined layers, which makes the proposed approach an interesting mean for estimating specific features in offshore hydrocarbon reservoir structures.

A novel transcranial MR Guided focused ultrasound method based on the ultrashort echo time skull acoustic model and phase retrieval techniques

Transcranial ultrasound stimulation (TUS) has been clinically applied as a neuromodulation tool. Particularly, the phase array ultrasound can be applied in TUS to non-invasively focus on the cortex or deep brain. However, the vital phase distortion of the ultrasound induced by the skull limits its clinical application. In the current study, we aimed to develop a hybrid method that combines the ultrashort echo time (UTE) magnetic resonance imaging (MRI) sequences with the prDeep technique to achieve focusing ventral intermediate thalamic nucleus (VIM). The time-reversal (TR) approach of the UTE numerical acoustic model of the skull combined with the prDeep algorithm was used to reduce the number of iterations. The skull acoustic model simulation therapy process was establish to valid this method’s prediction and focus performance, and the classical TR method were considered as the gold standard (GS). Our approach could restore 75% of the GS intensity in 25 iteration steps, with a superior the noise immunity. Our findings demonstrate that the phase aberration caused by the skull can be estimated using phase retrieval techniques to achieve a fast and accurate transcranial focus. The method has excellent adaptability and anti-noise capacity for satisfying complex and changeable scenarios.

Deep Learning-Based Acoustic Emission Signal Filtration Model in Reinforced Concrete

AbstractAcoustic emission is a nondestructive testing (NDT) technique, widely used to monitor the condition of structures for safety reasons especially in real time. The method utilizes the electrical signals generated by the elastic waves in a material under load to detect and locate damage in structures. However, identifying the sources of AE signals in concrete or composite materials can be challenging due to the anisotropic properties of materials and interpreting a large amount of AE data, leading to data misinterpretation and inaccurate detection of damage. Hence, the need for filtering out noise-induced signals from recorded data and emphasizing the actual AE source is crucial for monitoring and source localization of damage in real time. This study proposed a one-dimensional convolutional neural network (1D-CNN) deep learning approach to filter around 22,000 AE data in a reinforced concrete (RC) beam. The model utilizes significant AE parameters identified through neighborhood component analysis (NCA) to classify true AE signals from noise-induced signals. By using the optimized network parameters, a high classification accuracy of 97% and 96.29% was achieved during the training and testing phases, respectively. To check the reliability of the proposed AE filtering model in the real world, it was evaluated and verified using source location AE activities collected during a four-point bending test on a shear-deficient beam. The outcomes suggest that the proposed AE filtration model has the potential to accurately classify AE signals with an accuracy of 92.8% and proved that the filtration model provides accurate and valuable insight into source location determination.

A hybrid room acoustic modeling approach combining image source, acoustic diffusion equation, and time-domain discontinuous Galerkin methods

In this paper a hybrid model is introduced that constructs a broadband room impulse response using a geometrical (image source method) and a statistical method (acoustic diffusion equation) for the high-frequency range, supported by a wave-based method (time-domain discontinuous Galerkin method) for the low-frequency range. A crucial element concerns the construction of the high-frequency impulse response where a transition from a predominantly specular (image source) to a predominantly diffuse sound-field (diffusion equation) is required. To achieve this transition an analytical envelope is introduced. A key factor is the room-averaged scattering coefficient which accounts for all scattering behavior of the room and determines the speed of transition from a specular to a non-specular sound-field. To evaluate its performance, the model is compared to a broadband wave-based solver for two reference scenarios. The hybrid model shows promising results in terms of reverberation time (T20), center time (Ts) and bass-ratio (BR). Aspects such as the used geometrical complexity, the ‘room-averaged’ scattering coefficients, and other model simplifications and assumptions are discussed.