Hasil untuk "Acoustics. Sound"

Han Yang, Zhiliang Gao, Menglin Chen et al.

Polyphenols are natural compounds with diverse biological activities; however, their practical applications are often limited by poor solubility and chemical instability. In this study, a high-frequency ultrasound-assisted approach is developed for the preparation of polyphenol nanoparticles (NPs), producing well-dispersed and uniformly sized particles. The method exhibits excellent versatility and can be applied to a wide range of polyphenol precursors, including 1,8-dihydroxynaphthalene (1,8-DHN). Additionally, the integration of an ultrasound-assisted Fenton reaction markedly accelerates polyphenol polymerization and NP nucleation. The resulting NPs demonstrate outstanding antioxidant capacity, effectively scavenging intracellular reactive oxygen species (ROS). Notably, DHN-derived NPs show strong antibacterial activity, efficiently eliminating both Gram-positive (Staphylococcus aureus) and Gram-negative (Escherichia coli) bacteria at relatively low concentrations. Overall, this study presents a green, simple, and scalable strategy for fabricating multifunctional polyphenol NPs. The synergistic antioxidant and antibacterial properties of these NPs highlight their broad potential in biomedical engineering, providing a valuable platform for the design of next-generation bioactive nanomaterials.

Jeremy Zhengqi Huang, Emani Hicks, Sidharth et al.

For people with noise sensitivity, everyday soundscapes can be overwhelming. Existing tools such as active noise cancellation reduce discomfort by suppressing the entire acoustic environment, often at the cost of awareness of surrounding people and events. We present Sona, an interactive mobile system for real-time soundscape mediation that selectively attenuates bothersome sounds while preserving desired audio. Sona is built on a target-conditioned neural pipeline that supports simultaneous attenuation of multiple overlapping sound sources, overcoming the single-target limitation of prior systems. It runs in real time on-device and supports user-extensible sound classes through in-situ audio examples, without retraining. Sona is informed by a formative study with 68 noise-sensitive individuals. Through technical benchmarking and an in-situ study with 10 participants, we show that Sona achieves low-latency, multi-target attenuation suitable for live listening, and enables meaningful reductions in bothersome sounds while maintaining awareness of surroundings. These results point toward a new class of personal AI systems that support comfort and social participation by mediating real-world acoustic environments.

Ju-Hee Lee, Haesol Kwak, Eunjik Kim et al.

Acoustic metamaterials (AMs) are artificially structured materials composed of subwavelength units that enable acoustic phenomena not achievable with conventional materials and structures. This review defines and presents a distinct category referred to as soft acoustic metamaterials (SAMs), which use soft materials or reconfigurable structures to achieve enhanced acoustic functionality. These systems make use of the inherent flexibility of their materials or the deformability of their geometry to support passive, active, and adaptive functions. To capture this structural and functional diversity, we introduce a three-dimensional classification that considers geometry, acoustic control mechanisms, and functional responsiveness as interrelated aspects. The geometry is classified into two-dimensional metasurfaces and three-dimensional bulk structures. The control mechanisms include local resonance, phase modulation, attenuation, and structural reconfiguration. The response type refers to whether the system behaves passively, actively, or adaptively. Using this approach, we provide an overview of representative implementations and compare different design approaches to highlight their working principles and application areas. This review presents a structured classification for soft acoustic metamaterials and offers a foundation for future research, with broad potential in intelligent sound systems, wearable acoustics, and architectural applications.

Rucz Péter, Fontestad Elena Esteve, Angster Judit et al.

Viscothermal effects at the walls are the dominant sources of loss in the air columns of various types of wind instruments. The classical theory of viscous and thermal boundary layers gives an analytical result that allows for computing the wall losses of acoustical wave propagation in cylindrical ducts. Based on theoretical considerations, a simple formula is derived for straight ducts that allows for taking the shape of the cross section into account in the wall loss coefficient. This unidimensional model is compared to three-dimensional finite element computations of different geometries, and an excellent agreement is found. As an application of the theory, a revised scaling method for wooden flue organ pipes with rectangular cross sections is elaborated. In organ building practice, wooden pipes are often made narrower than the reference width because of space limitations. Organ builders reported an undesired change of timbre for narrow pipes, which may be explained by the increased amount of wall losses. The proposed scaling approach enables designing narrower wooden pipes with keeping the amount of wall losses the same as of a reference pipe. Two series of experimental organ pipes designed following the traditional and new scaling rules are examined and compared by means of acoustical measurements and sound analyses, proving the practical applicability of the proposed scaling method.

Jiaxing Zheng, Yuzhu Zha, Mengyu Feng et al.

Understanding the formation and interactions of out-of-phase cavitation bubbles is crucial for comprehensively exploring cavitation processes in both nature and engineering applications. In this study, a numerical model for the interaction of out-of-phase cavitation bubbles is developed using the hybrid thermal lattice Boltzmann method, where cavitation bubbles are solely excited by thermal nucleation. Furthermore, a new temperature distribution function for thermal nucleation is proposed, enabling a more stable generation of cavitation bubbles. By comparing the results with those obtained from the Rayleigh–Plesset equation incorporating the thermal effect term, the validity of the thermal nucleation model has been verified. Subsequently, the validity of two out-of-phase cavitation bubbles model is experimentally verified, and the dynamic and thermodynamic behaviors of two out-of-phase cavitation bubbles are systematically investigated. The behaviors are primarily influenced by the dimensionless bubble spacing l0∗ and the dimensionless phase difference Δθ∗. Specifically, when l0∗≥1.00, weak interaction is observed, and no penetration phenomenon occurs. When l0∗<1.00 and Δθ∗<0.50, strong interaction is observed, and a penetration phenomenon occurs. Finally, the jet-enhancement effect of two out-of-phase cavitation bubbles is explored. The results indicate that when l0∗=0.78, the optimal jet-enhancement effect can be achieved by maintaining Δθ∗=0.67. These findings provide important numerical insights for optimizing jet-enhancement in cavitation-related technologies.

Xue-Chen Dong, Qiu-Yu Ou, Yi-Han Wu et al.

This paper introduces a generalized integral transform that encompasses the Laplace, Fourier, and numerous contemporary integral transforms as particular instances, while preserving their defining characteristics. Secondly, we propose the application of the generalized integral transform in the variational iteration method for the straightforward identification of the Lagrange multiplier, thus enabling the resolution of nonlinear oscillator problems. Finally, we present a series of illustrative examples to demonstrate the efficacy of this approach.

Giovanni Bologni, Richard Heusdens, Richard C. Hendriks

Acoustic beamforming models typically assume wide-sense stationarity of speech signals within short time frames. However, voiced speech is better modeled as a cyclostationary (CS) process, a random process whose mean and autocorrelation are $T_1$-periodic, where $α_1=1/T_1$ corresponds to the fundamental frequency of vowels. Higher harmonic frequencies are found at integer multiples of the fundamental. This work introduces a cyclic multichannel Wiener filter (cMWF) for speech enhancement derived from a cyclostationary model. This beamformer exploits spectral correlation across the harmonic frequencies of the signal to further reduce the mean-squared error (MSE) between the target and the processed input. The proposed cMWF is optimal in the MSE sense and reduces to the MWF when the target is wide-sense stationary. Experiments on simulated data demonstrate considerable improvements in scale-invariant signal-to-distortion ratio (SI-SDR) on synthetic data but also indicate high sensitivity to the accuracy of the estimated fundamental frequency $α_1$, which limits effectiveness on real data.

Timothy J. Mason, Daniela Ghimpeteanu, Ioan Călinescu et al.

The most used piece of equipment for sonochemistry is the ultrasonic cleaning bath. However, what is sometimes forgotten by scientists new to sonochemistry is the vital importance of the shape and positioning of any reaction vessel in the bath to obtain the most efficient and reproducible results. In experiments using an ultrasonic bath, a glass vessel (reactor) is inserted into the water contained in the bath. The water acts as the coupling medium for the transfer of acoustic energy from the transducer to the vessel (termed indirect sonication). The position of the reaction vessel above the base of the US bath can change the energy transmitted into it over a wide range of values (in our system between 100–500 J). We have carried out a study of the vertical distribution of the ultrasound field in a common type of ultrasound bath, comparing conventional sonochemistry dosimeters with a new and very simple approach using the Ultrasonic Capillary Effect (UCE) which can be performed in any laboratory. The technique involves the use of a capillary tube, to locate the vertical positions of acoustic pressure maxima above a single transducer on the base of the bath. The results are compared with those obtained using calorimetry, iodimetry, a cavitometer and the perforation of aluminium foil. The results show that the optimum position for the reaction vessel can be located very simply using UCE.

Liang Yang, Xiaoli Jia, Xiangning Liao et al.

Exhaust sound quality is an important part of vehicle performance. In this paper, the sporty exhaust sound quality of an economical vehicle equipped with a 4-cylinder and 4-stroke engine is evaluated, analyzed, and improved under acceleration. Firstly, a sporty feeling evaluation method with engine speed divided is proposed, and the influence of exhaust sound order components on sporty exhaust sound is analyzed. The results show that while the A-weighted sound pressure level (ASPL) of Order 2 is lower and the ASPLs of Orders 4 and 6 are higher, the exhaust sound is sportier. Then, a hybrid predicted model of vehicle sporty exhaust sound under acceleration is established based on convolutional neural network (CNN) and support vector regression (SVR) algorithm. The relative errors between the predicted results of CNN-SVR hybrid model and the subjective evaluation results are limited within 2%, which indicates that the CNN-SVR hybrid prediction model achieves a high accuracy in assessing the sporty feeling of exhaust sound. Finally, considering the frequency ranges corresponding with the above order components under the practical accelerating condition, a strategy is proposed to enhance the sporty feeling of exhaust sound by reducing the sound energy within 100 Hz and increasing the sound energy within 100–450 Hz. Based on this strategy, a muffler with different structure is selected and installed on the economical vehicle, and the sporty feeling of exhaust sound is 0.63 points higher than before.

Carlos Hernandez-Olivan, Marc Delcroix, Tsubasa Ochiai et al.

Binaural target sound extraction (TSE) aims to extract a desired sound from a binaural mixture of arbitrary sounds while preserving the spatial cues of the desired sound. Indeed, for many applications, the target sound signal and its spatial cues carry important information about the sound source. Binaural TSE can be realized with a neural network trained to output only the desired sound given a binaural mixture and an embedding characterizing the desired sound class as inputs. Conventional TSE systems are trained using signal-level losses, which measure the difference between the extracted and reference signals for the left and right channels. In this paper, we propose adding explicit spatial losses to better preserve the spatial cues of the target sound. In particular, we explore losses aiming at preserving the interaural level (ILD), phase (IPD), and time differences (ITD). We show experimentally that adding such spatial losses, particularly our newly proposed ITD loss, helps preserve better spatial cues while maintaining the signal-level metrics.

Zhisheng Zheng, Puyuan Peng, Ziyang Ma et al.

Spatial sound reasoning is a fundamental human skill, enabling us to navigate and interpret our surroundings based on sound. In this paper we present BAT, which combines the spatial sound perception ability of a binaural acoustic scene analysis model with the natural language reasoning capabilities of a large language model (LLM) to replicate this innate ability. To address the lack of existing datasets of in-the-wild spatial sounds, we synthesized a binaural audio dataset using AudioSet and SoundSpaces 2.0. Next, we developed SpatialSoundQA, a spatial sound-based question-answering dataset, offering a range of QA tasks that train BAT in various aspects of spatial sound perception and reasoning. The acoustic front end encoder of BAT is a novel spatial audio encoder named Spatial Audio Spectrogram Transformer, or Spatial-AST, which by itself achieves strong performance across sound event detection, spatial localization, and distance estimation. By integrating Spatial-AST with LLaMA-2 7B model, BAT transcends standard Sound Event Localization and Detection (SELD) tasks, enabling the model to reason about the relationships between the sounds in its environment. Our experiments demonstrate BAT's superior performance on both spatial sound perception and reasoning, showcasing the immense potential of LLMs in navigating and interpreting complex spatial audio environments.

Rufin Makarewicz, Maciej Buszkiewicz

Amplitude modulated noise from a single wind turbine is considered. The time-varying modulation depth D_m and the short time-average sound level L_Aeq,τ (with τ = 20 s) are measured at the reference distance d_*. Due to amplitude modulation, a penalty has to be added to L_Aeq,τ. The paper shows how to calculate the corrected long-term time-average sound level L ̂_AeqT (with T >> 20 s), which accounts for amplitude modulation, at any distance d ≠ d_* from the wind turbine. The proposed methodology needs to be tested by research

Song Chun-sheng, Xiong Xue-chun, Yang Qi et al.

The power unit on board the ship generates periodic low-frequency vibration that affects the normal operation of the equipment on board, and the adaptive feedforward control algorithm can effectively suppress such harmful vibration noise. But the adaptive feedforward control algorithm needs to obtain the identification model of the secondary channels, and the frequency domain least squares method based on the linear Extended auto-regressive model (ARX) is difficult to obtain the identification model with nonlinear characteristics. The nonlinear auto-regressive model (NARX) adds nonlinear mapping layers to the topology of the ARX model to enhance the identification capability of the NARX model for complex systems. In this paper, a block diagram of the Fx-LMS feedforward control algorithm based on the NARX model is proposed, then the initial parameters of the NARX neural network are optimized using the Quantum Particle Swarm Optimization (QPSO) algorithm and the secondary channel is identified, and the identification results show that the accuracy of identifying the secondary channel using the NARX neural network is higher than that of the ARX model. The simulation and experimental results show that the vibration damping effect of the proposed method is better than the traditional Fx-LMS method for both single-line spectrum and multi-line spectrum periodic low-frequency disturbances, which provides a new method for the suppression of periodic low-frequency disturbances.

Lixin Kang, Qiufang Liang, Arif Rashid et al.

A novel food packaging film was developed by incorporating a tea polyphenols-loaded pullulan/trehalose (TP@Pul/Tre) into a composite film with ultrasound-assisted treatment of dual-frequency (20/35 kHz, 40 W/L) for 15 min to assess the physicochemical and mechanical properties of a composite film. The optimized ultrasound-assisted significantly increases elongation at break, tensile strength, and improves the composite film's UV/water/oxygen barrier properties. Structure analysis using attenuated total reflectance-Fourier transform infrared, X-ray diffraction and thermal stability revealed that these improvements were achieved through ultrasound-enhanced H-bonds, more ordered molecular arrangements, and good intermolecular compatibility. Besides, the ultrasound-assisted TP@Pul/Tre film has proven to have good antibacterial performance against Escherichia coli and Staphylococcus aureus, with approximately 100 % lethality at 4 h and 8 h, respectively. Moreover, the ultrasound-assisted TP@Pul/Tre film effectively delayed moisture loss, oxidative browning, decay, and deterioration in fresh-cut apples and pears, thereby extending their shelf life. Thus, ultrasound has proved to be an effective tool for improving the quality of food packaging films, with a wide range of applications.

Ilyass Moummad, Romain Serizel, Nicolas Farrugia

Bioacoustic sound event detection allows for better understanding of animal behavior and for better monitoring biodiversity using audio. Deep learning systems can help achieve this goal, however it is difficult to acquire sufficient annotated data to train these systems from scratch. To address this limitation, the Detection and Classification of Acoustic Scenes and Events (DCASE) community has recasted the problem within the framework of few-shot learning and organize an annual challenge for learning to detect animal sounds from only five annotated examples. In this work, we regularize supervised contrastive pre-training to learn features that can transfer well on new target tasks with animal sounds unseen during training, achieving a high F-score of 61.52%(0.48) when no feature adaptation is applied, and an F-score of 68.19%(0.75) when we further adapt the learned features for each new target task. This work aims to lower the entry bar to few-shot bioacoustic sound event detection by proposing a simple and yet effective framework for this task, by also providing open-source code.

Hualei Wang, Jianguo Mao, Zhifang Guo et al.

Large language models reveal deep comprehension and fluent generation in the field of multi-modality. Although significant advancements have been achieved in audio multi-modality, existing methods are rarely leverage language model for sound event detection (SED). In this work, we propose an end-to-end framework for understanding audio features while simultaneously generating sound event and their temporal location. Specifically, we employ pretrained acoustic models to capture discriminative features across different categories and language models for autoregressive text generation. Conventional methods generally struggle to obtain features in pure audio domain for classification. In contrast, our framework utilizes the language model to flexibly understand abundant semantic context aligned with the acoustic representation. The experimental results showcase the effectiveness of proposed method in enhancing timestamps precision and event classification.

Jian Guan, Feiyang Xiao, Youde Liu et al.

Existing contrastive learning methods for anomalous sound detection refine the audio representation of each audio sample by using the contrast between the samples' augmentations (e.g., with time or frequency masking). However, they might be biased by the augmented data, due to the lack of physical properties of machine sound, thereby limiting the detection performance. This paper uses contrastive learning to refine audio representations for each machine ID, rather than for each audio sample. The proposed two-stage method uses contrastive learning to pretrain the audio representation model by incorporating machine ID and a self-supervised ID classifier to fine-tune the learnt model, while enhancing the relation between audio features from the same ID. Experiments show that our method outperforms the state-of-the-art methods using contrastive learning or self-supervised classification in overall anomaly detection performance and stability on DCASE 2020 Challenge Task2 dataset.

Alexandre L'Her, Angélique Drémeau, Florent Le Courtois et al.

Numerical models are currently used to understand how environmental fluctuations impact acoustic propagation. Such a process can be tedious in complex fluctuating environments. This letter proposes a complementary approach based upon canonical correlation analysis (CCA) to determine statistical relationships between two sets of observed acoustic and oceanographic variables. It is shown, as an example, how CCA puts forward the impact of external and internal tide on shallow water propagation. Results are consistent with the physical understanding of tide impact on acoustic propagation. They encourage the use of CCA for complex studies, in particular, for environments fluctuating under several environmental phenomena.

Jisheng Bai, Jianfeng Chen, Mou Wang et al.

Anomalous sound detection for machine condition monitoring has great potential in the development of Industry 4.0. However, these anomalous sounds of machines are usually unavailable in normal conditions. Therefore, the models employed have to learn acoustic representations with normal sounds for training, and detect anomalous sounds while testing. In this article, we propose a self-supervised dual-path Transformer (SSDPT) network to detect anomalous sounds in machine monitoring. The SSDPT network splits the acoustic features into segments and employs several DPT blocks for time and frequency modeling. DPT blocks use attention modules to alternately model the interactive information about the frequency and temporal components of the segmented acoustic features. To address the problem of lack of anomalous sound, we adopt a self-supervised learning approach to train the network with normal sound. Specifically, this approach randomly masks and reconstructs the acoustic features, and jointly classifies machine identity information to improve the performance of anomalous sound detection. We evaluated our method on the DCASE2021 task2 dataset. The experimental results show that the SSDPT network achieves a significant increase in the harmonic mean AUC score, in comparison to present state-of-the-art methods of anomalous sound detection.

Anastasia Natsiou, Sean O'Leary

The rise of deep learning algorithms has led many researchers to withdraw from using classic signal processing methods for sound generation. Deep learning models have achieved expressive voice synthesis, realistic sound textures, and musical notes from virtual instruments. However, the most suitable deep learning architecture is still under investigation. The choice of architecture is tightly coupled to the audio representations. A sound's original waveform can be too dense and rich for deep learning models to deal with efficiently - and complexity increases training time and computational cost. Also, it does not represent sound in the manner in which it is perceived. Therefore, in many cases, the raw audio has been transformed into a compressed and more meaningful form using upsampling, feature-extraction, or even by adopting a higher level illustration of the waveform. Furthermore, conditional on the form chosen, additional conditioning representations, different model architectures, and numerous metrics for evaluating the reconstructed sound have been investigated. This paper provides an overview of audio representations applied to sound synthesis using deep learning. Additionally, it presents the most significant methods for developing and evaluating a sound synthesis architecture using deep learning models, always depending on the audio representation.