Hasil untuk "Acoustics. Sound"

Bilkent Samsurya

Accurate sound propagation simulation is essential for delivering immersive experiences in virtual applications, yet industry methods for acoustic modeling often do not account for the full breadth of acoustic wave phenomena. This paper proposes a novel two-dimensional (2D) finite-difference time-domain (FDTD) framework that simulates sound propagation as a wave-based model in Unreal Engine, with an emphasis on capturing lower frequency wave phenomena, embedding occlusion, diffraction, reflection and interference in generated impulse responses. The process begins by discretizing the scene geometry into a 2D grid via a top-down projection from which obstacle masks and boundary conditions are derived. A Python-based FDTD solver injects a sine sweep at a source position, and virtual quadraphonic microphone arrays record pressure field responses at pre-defined listener positions. De-convolution of the pressure responses yields multi-channel impulse responses that retain spatial directionality which are then integrated into Unreal Engine's audio pipeline for dynamic playback. Benchmark tests confirm agreement with analytical expectations, and the paper outlines hybrid extensions aimed at commercial viability.

D. Veerababu, Prasanta K. Ghosh

The study of sound propagation in a uniform duct having a mean flow has many applications, such as in the design of gas turbines, heating, ventilation and air conditioning ducts, automotive intake and exhaust systems, and in the modeling of speech. In this paper, the convective effects of the mean flow on the plane wave acoustic field inside a uniform duct were studied using artificial neural networks. The governing differential equation and the associated boundary conditions form a constrained optimization problem. It is converted to an unconstrained optimization problem and solved by approximating the acoustic field variable to a neural network. The complex-valued acoustic pressure and particle velocity were predicted at different frequencies, and validated against the analytical solution and the finite element models. The effect of the mean flow is studied in terms of the acoustic impedance. A closed-form expression that describes the influence of various factors on the acoustic field is derived.

Stefan Fichna, Steven van de Par, Bernhard U. Seeber et al.

Virtual acoustic environments enable the creation and simulation of realistic and eco-logically valid daily-life situations vital for hearing research and audiology. Reverberant indoor environments are particularly important. For real-time applications, room acous-tics simulation requires simplifications, however, the necessary acoustic level of detail (ALOD) remains unclear in order to capture all perceptually relevant effects. This study examines the impact of varying ALOD in simulations of three real environments: a living room with a coupled kitchen, a pub, and an underground station. ALOD was varied by generating different numbers of image sources for early reflections, or by excluding geo-metrical room details specific for each environment. Simulations were perceptually eval-uated using headphones in comparison to binaural room impulse responses measured with a dummy head in the corresponding real environments, or by using loudspeakers. The study assessed the perceived overall difference for a pulse stimulus, a played electric bass and a speech token. Additionally, plausibility, speech intelligibility, and externaliza-tion were evaluated. Results indicate that a strong reduction in ALOD is feasible while maintaining similar plausibility, speech intelligibility, and externalization as with dummy head recordings. The number and accuracy of early reflections appear less relevant, pro-vided diffuse late reverberation is appropriately represented.

Jiawen Huang, Emmanouil Benetos

Abstract This paper introduces singing to speech conversion (S2S), a cross-domain voice conversion task, and presents the first deep learning-based S2S system. S2S aims to transform singing into speech while retaining the phonetic information, reducing variations in pitch, rhythm, and timbre. Inspired by the Glow-TTS architecture, the proposed model is built using generative flow, with an adjusted alignment module between the latent features. We adapt the original monotonic alignment search (MAS) to the S2S scenario and utilize a duration predictor to deal with the duration differences between the two modalities. Subjective evaluations show that the proposed model outperforms signal processing baselines in naturalness and outperforms a transcribe-and-synthesize baseline in phonetic similarity to the original singing. We further demonstrate that singing-to-speech could be an effective augmentation method for low-resource lyrics transcription.

Ziyang Wang, Demin Liu, Bin Ji et al.

Hydrodynamic cavitation (HC) surge accompanied by strong swirl is widely present in fluid machinery and has become a hot topic in many engineering fields. This paper employed a two-way coupling Eulerian-Lagrangian multiscale cavitation modeling method to investigate the unsteady characteristics of HC surge under two typical conditions in a diffuser with swirling flow. Compared with the available experimental data, the quasi-periodic growth, shedding and fragmentation of HC surge into discrete bubbles are well reproduced by the multiscale cavitation modeling method. Results show that reducing the cavitation number, σ, increases the length of the axisymmetric structure and moves the breakdown point downstream; reducing the swirl number, Sw, intensifies the rotation and distortion of the spiral structure. The bubbles tend to move in a spiral shape and are significantly affected by the backflow. The combined spectrum analysis of cavity volume evolution and pressure fluctuation reveals two typical peaks. The cavity shedding frequency, fⅠ, and vortex rope motion frequency, fⅡ, of cavitation-dominated flow are smaller than those of the swirl-dominated flow. Under each operating condition, the vortex rope motion frequency, fⅡ, in the spiral vortex region is lower than in the axisymmetric vortex region. The bubble rebound phenomenon significantly increases the medium- and high-frequency intensity of pressure fluctuations. Further analysis suggests that a decrease in the cavitation number strengthens the velocity gradient in the diffusion section and promotes the backflow development, intensifying the cavitation-vortex-backflow interaction. A decrease in swirl number increases the vorticity magnitude, while cavitation intensifies the vorticity fluctuation.

Dame Fall, Marc Duquennoy, Nikolay Smagin et al.

This study presents a non-destructive method for estimating surface acoustic wave attenuation, which is highly sensitive to microstructural features, especially at high frequencies. The method uses a single wideband dispersive interdigital transducer (IDT) that remotely emits acoustic waves at the sample's edge. Chirp compression of the temporal displacement response is achieved by correlating the excitation signal with the spatial configuration of the IDT's electrodes. This technique generates high-amplitude pulses with a sufficient signal-to-noise ratio, critical for enabling accurate attenuation estimation over a frequency range (15–70 MHz). Results from nickel and aluminum demonstrate the method's effectiveness for rapid material characterization.

Zheng Li, Weibin You, Sivakumar Manickam et al.

To effectively eliminate excess antibiotics from aqueous environments and to mitigate the dissemination of antibiotic resistance genes (ARGs), this study proposes a novel degradation system that activates peroxymonosulfate (PMS) through a synergistic combination of hydrodynamic cavitation (HC) and divalent copper ions (Cu2+). Levofloxacin (LEV) is employed as the representative target contaminant to evaluate the system’s performance. HC has emerged as a promising technique for pollutant removal. In this study, the localized high-temperature and high-pressure conditions generated by HC not only partially activated PMS but also facilitated its interaction with Cu2+ ions, leading to a pronounced synergistic enhancement in sulfate radical (SO4·−) generation and efficient pollutant degradation. Under optimized HC/Cu2+/PMS conditions (Cu2+ = 5 mM, PMS = 2.5 mM, inlet pressure = 0.15 MPa, pH = 10), complete removal of LEV (30 mg/L) was achieved within 50 min. This study elucidates the degradation mechanisms and pathways of LEV within the coupled HC/Cu2+/PMS system and evaluates the ecological safety of its degradation intermediates using the U.S. EPA’s T.E.S.T. (Toxicity Estimation Software Tool). Furthermore, the system’s applicability was validated through degradation experiments involving a range of representative pollutants, demonstrating its broad-spectrum effectiveness. Crucially, the HC/Cu2+/PMS system demonstrated a superior cavitation yield (2.78 × 10−5 mg/J) and a low electrical energy per order (EE/O) of 229.48 kWh/m3, highlighting its high energy efficiency and practical potential for sustainable wastewater treatment. The experimental results emphasize the system’s strong potential for the effective removal of organic pollutants from water, offering a novel and sustainable approach for advanced water treatment.

Hounsu Kim, Soonbeom Choi, Juhan Nam

Synthesizing performing guitar sound is a highly challenging task due to the polyphony and high variability in expression. Recently, deep generative models have shown promising results in synthesizing expressive polyphonic instrument sounds from music scores, often using a generic MIDI input. In this work, we propose an expressive acoustic guitar sound synthesis model with a customized input representation to the instrument, which we call guitarroll. We implement the proposed approach using diffusion-based outpainting which can generate audio with long-term consistency. To overcome the lack of MIDI/audio-paired datasets, we used not only an existing guitar dataset but also collected data from a high quality sample-based guitar synthesizer. Through quantitative and qualitative evaluations, we show that our proposed model has higher audio quality than the baseline model and generates more realistic timbre sounds than the previous leading work.

Neil Jerome A. Egarguin, Daniel Onofrei

In this paper, we present a time domain extension of our strategy on manipulating radiated scalar Helmholtz fields and discuss two important applied scenarios, namely (1) creating personal sound zones inside a bounded domain and (2) shielded localized communication. Our strategy is based on the authors' previous works establishing the possibility and stability of controlling acoustic fields using an array of almost non-radiating coupling sources and presents a detailed Fourier synthesis approach towards a time-domain effect. We require that the array of acoustic sources creates the desired fields on the control regions while maintaining a zero field beyond a larger circumscribed sphere. This paper recalls the main theoretical results then presents the underlying Fourier synthesis paradigm and show, through relevant simulations, the performance of our strategy.

Panagiota Anastasopoulou, Jessica Torrey, Xavier Serra et al.

Automatic sound classification has a wide range of applications in machine listening, enabling context-aware sound processing and understanding. This paper explores methodologies for automatically classifying heterogeneous sounds characterized by high intra-class variability. Our study evaluates the classification task using the Broad Sound Taxonomy, a two-level taxonomy comprising 28 classes designed to cover a heterogeneous range of sounds with semantic distinctions tailored for practical user applications. We construct a dataset through manual annotation to ensure accuracy, diverse representation within each class and relevance in real-world scenarios. We compare a variety of both traditional and modern machine learning approaches to establish a baseline for the task of heterogeneous sound classification. We investigate the role of input features, specifically examining how acoustically derived sound representations compare to embeddings extracted with pre-trained deep neural networks that capture both acoustic and semantic information about sounds. Experimental results illustrate that audio embeddings encoding acoustic and semantic information achieve higher accuracy in the classification task. After careful analysis of classification errors, we identify some underlying reasons for failure and propose actions to mitigate them. The paper highlights the need for deeper exploration of all stages of classification, understanding the data and adopting methodologies capable of effectively handling data complexity and generalizing in real-world sound environments.

Siqi Zhao, Yubo Zhao, Haotian Liu et al.

In this study, the effects of high-intensity ultrasound (HIU) treatment combined with hydrogen peroxide (H2O2) addition on the thermal stability of myofibrillar protein (MP)–stabilized emulsions in low-salt conditions were investigated. Results showed that compared to using either HIU or H2O2 treatment alone, HIU treatment combined with H2O2 was most effective in enhancing the physical stability of emulsions. Moreover, the emulsion stabilized by MPs co-treated with HIU and H2O2 exhibited the most uniform distribution, highest absolute zeta potential, and optimal rheological properties upon heating. This combination effect during heating was caused by the inhibition of disulfide bond cross-linking of myosin heads by H2O2 and the dissociation of filamentous myosin structures using the HIU treatment. In addition, the results of oxidative stability analysis indicated that the addition of H2O2 increased the content of oxidation products; however, the overall influence on the oxidative stability of emulsions was not significant. In conclusion, the combination of HIU and H2O2 treatment is a promising approach to suppress heat-induced MP aggregation and improve the thermal stability of corresponding emulsions.

Jia Ge, Zongxin Mo, Shuangyang Zhang et al.

Photoacoustic tomography (PAT), as a novel medical imaging technology, provides structural, functional, and metabolism information of biological tissue in vivo. Sparse Sampling PAT, or SS-PAT, generates images with a smaller number of detectors, yet its image reconstruction is inherently ill-posed. Model-based methods are the state-of-the-art method for SS-PAT image reconstruction, but they require design of complex handcrafted prior. Owing to their ability to derive robust prior from labeled datasets, deep-learning-based methods have achieved great success in solving inverse problems, yet their interpretability is poor. Herein, we propose a novel SS-PAT image reconstruction method based on deep algorithm unrolling (DAU), which integrates the advantages of model-based and deep-learning-based methods. We firstly provide a thorough analysis of DAU for PAT reconstruction. Then, in order to incorporate the structural prior constraint, we propose a nested DAU framework based on plug-and-play Alternating Direction Method of Multipliers (PnP-ADMM) to deal with the sparse sampling problem. Experimental results on numerical simulation, in vivo animal imaging, and multispectral un-mixing demonstrate that the proposed DAU image reconstruction framework outperforms state-of-the-art model-based and deep-learning-based methods.

Christopher Ick, Brian McFee

As deeper and more complex models are developed for the task of sound event localization and detection (SELD), the demand for annotated spatial audio data continues to increase. Annotating field recordings with 360$^{\circ}$ video takes many hours from trained annotators, while recording events within motion-tracked laboratories are bounded by cost and expertise. Because of this, localization models rely on a relatively limited amount of spatial audio data in the form of spatial room impulse response (SRIR) datasets, which limits the progress of increasingly deep neural network based approaches. In this work, we demonstrate that simulated geometrical acoustics can provide an appealing solution to this problem. We use simulated geometrical acoustics to generate a novel SRIR dataset that can train a SELD model to provide similar performance to that of a real SRIR dataset. Furthermore, we demonstrate using simulated data to augment existing datasets, improving on benchmarks set by state of the art SELD models. We explore the potential and limitations of geometric acoustic simulation for localization and event detection. We also propose further studies to verify the limitations of this method, as well as further methods to generate synthetic data for SELD tasks without the need to record more data.

Manjunath Mulimani, Annamaria Mesaros

In this paper, we propose a method for incremental learning of two distinct tasks over time: acoustic scene classification (ASC) and audio tagging (AT). We use a simple convolutional neural network (CNN) model as an incremental learner to solve the tasks. Generally, incremental learning methods catastrophically forget the previous task when sequentially trained on a new task. To alleviate this problem, we propose independent learning and knowledge distillation (KD) between the timesteps in learning. Experiments are performed on TUT 2016/2017 dataset, containing 4 acoustic scene classes and 25 sound event classes. The proposed incremental learner first solves the ASC task with an accuracy of 94.0%. Next, it learns to solve the AT task with an F1 score of 54.4%. At the same time, its performance on the previous ASC task decreases only by 5.1 percentage points due to the additional learning of the AT task.

Yunyi Liu, Craig Jin, David Gunawan

Controlling the variations of sound effects using neural audio synthesis models has been a difficult task. Differentiable digital signal processing (DDSP) provides a lightweight solution that achieves high-quality sound synthesis while enabling deterministic acoustic attribute control by incorporating pre-processed audio features and digital synthesizers. In this research, we introduce DDSP-SFX, a model based on the DDSP architecture capable of synthesizing high-quality sound effects while enabling users to control the timbre variations easily. We propose a transient modelling technique with higher objective evaluation scores and subjective ratings over impulsive signals (footsteps, gunshots). We propose a simple method that achieves timbre variation control while also allowing deterministic attribute control. We further qualitatively show the timbre transfer performance using voice as the guiding sound.

D. Mouloua, M. Lejeune, N.S. Rajput et al.

The photocatalytic degradation of methylene blue is a straightforward and cost-effective solution for water decontamination. Although many materials have been reported so far for this purpose, the proposed solutions inflicted high fabrication costs and low efficiencies. Here, we report on the synthesis of tetragonal (1T) and hexagonal (2H) mixed molybdenum disulfide (MoS2) heterostructures for an improved photocatalytic degradation efficiency by means of a single-step chemical vapor deposition (CVD) technique. We demonstrate that the 1T-MoS2/2H-MoS2 heterostructures exhibited a narrow bandgap ∼ 1.7 eV, and a very low reflectance (<5%) under visible-light, owing to their particular vertical micro-flower-like structure. We exfoliated the CVD-synthesised 1T-MoS2/2H-MoS2 films to assess their photodegradation properties towards the standard methylene blue dye. Our results showed that the photo-degradation rate-constant of the 1T-MoS2/2H-MoS2 heterostructures is much greater under UV excitation (i.e., 12.5 × 10−3 min−1) than under visible light illumination (i.e., 9.2 × 10−3 min−1). Our findings suggested that the intermixing of the conductive 1T-MoS2 with the semi-conducting 2H-MoS2 phases favors the photogeneration of electron-hole pairs. More importantly, it promotes a higher efficient charge transfer, which accelerates the methylene blue photodegradation process.

Qianru Yang, Lulu Yang, Chihan Peng et al.

Testicular torsion (TT) is a medical emergency that requires immediate diagnostic evaluation. Photoacoustic imaging (PAI) has the potential to provide spatially resolved oxygen saturation (sO2), which can serve as a valuable marker in TT diagnosis. We investigated the potential of PAI as an alternative method for TT diagnosis and testicular injury assessment. We measured sO2 levels in different degrees of TT models using PAI at various time points. Based on histopathological results, we found that the averaged sO2 per pixel (sO2̅) and reduction of sO2̅ (rsO2) in twisted testicles had significant correlations with hypoxic conditions. Both sO2̅ and rsO2 exhibited excellent diagnostic abilities in detecting TT and identifying ischemia/hypoxia injury following TT. Furthermore, PAI-measured sO2 demonstrated favorable diagnostic capabilities in discriminating if the testicle had suffered irreversible injury. In summary, PAI presents a potentially promising novel approach in evaluating TT and warrants further clinical investigation.

Bellia Angela

The aim of this work is to analyse recent studies which have raised new hypotheses concerning aural architecture as an emerging trend in humanities research, with a particular focus on the intersection of sacred space, rituals, and sound in the past. These studies have highlighted how sacred buildings not only defined a sacred place as a physical and symbolic expression of a specific form of worship but also established the setting for performative and multisensorial ceremonies in which music, dance, and other sonic events played an important role. In this contribution, we investigate studies on aural architecture to explore if the location of sacred spaces indicates whether ancient people reacted to ritual and musical developments by modifying sanctuaries or by designing and constructing new buildings and spaces for performances. In addition, this article explores studies on aural architecture to obtain an overview of how specific sonic features could have influenced the soundscape of sacred spaces, which consisted not only of songs, music, prayers, recitations and religious sonic and vocal utterances but also of natural elements, such as animals, water, and wind. This overview also takes into consideration how digital technologies and virtual acoustics can help shape our understanding of the architecture-sound nexus.

Junfei Li, Wenqi Wang, Yangbo Xie et al.

Jinhui Fan, Fei Wang

The two-dimensional distribution of gas-solid flow parameters is a great research significance to reflect the actual situation in industry. The commonly used method is the ultrasonic tomography method, in which multiple probes are arranged at various angles, or the measurement device is rotated as that in medicine, but in most industrial situations, it is impossible to install probes at all angles or rotate the measured pipe. The backscattering method, however, uses only one transducer to both transmit and receive signals, and the twodimensional information is obtained by only rotating the transducer. Ultrasound attenuates greatly in the air, and the attenuation changes with frequency. Therefore, COMSOL is used to study the reflection of particles with different radii in the air to ultrasound with various frequencies. It is found that the backscattering equivalent voltage is the largest when the product of ultrasonic frequency and particle radius is about 27.78 Hz �� m, and the particle concentration of 30% causes the strongest backscattering. The simulated results are in good agreement with the Faran backscattering model, which can provide references for selecting the appropriate frequency and obtaining the concentration when measuring gas-solid two-phase flow with the ultrasonic backscattering method.