Hasil untuk "Acoustics. Sound"

R. Fleury, D. Sounas, A. Alú

Sensing an incoming signal is typically associated with absorbing a portion of its energy, inherently perturbing the measurement and creating reflections and shadows. Here, in contrast, we demonstrate a non-invasive, shadow-free, invisible sensor for airborne sound waves at audible frequencies, which fully absorbs the impinging signal, without at the same time perturbing its own measurement or creating a shadow. This unique sensing device is based on the unusual scattering properties of a parity-time (PT) symmetric metamaterial device formed by a pair of electro-acoustic resonators loaded with suitably tailored non-Foster electrical circuits, constituting the acoustic equivalent of a coherent perfect absorber coupled to a coherent laser. Beyond the specific application to non-invasive sensing, our work broadly demonstrates the unique relevance of PT-symmetric metamaterials for acoustics, loss compensation and extraordinary wave manipulation. Any typical sensing device must absorb energy, thereby altering the measured signal as it propagates on. By exploiting parity-time symmetry via non-Foster circuits, Fleury et al. show that a sensor can be built that absorbs incoming signals without perturbing them or creating a shadow, rendering it invisible.

Michael Vorlnder

T. Houtgast, H. Steeneken

M. Miyoshi, Yutaka Kaneda

Sripathi Sridhar, Prem Seetharaman, Oriol Nieto et al.

Sound designers search for sounds in large sound effects libraries using aspects such as sound class or visual context. However, the metadata needed for such search is often missing or incomplete, and requires significant manual effort to add. Existing solutions to automate this task by generating metadata, i.e. captioning, and search using learned embeddings, i.e. text-audio retrieval, are not trained on metadata with the structure and information pertinent to sound design. To this end we propose audiocards, structured metadata grounded in acoustic attributes and sonic descriptors, by exploiting the world knowledge of LLMs. We show that training on audiocards improves downstream text-audio retrieval, descriptive captioning, and metadata generation on professional sound effects libraries. Moreover, audiocards also improve performance on general audio captioning and retrieval over the baseline single-sentence captioning approach. We release a curated dataset of sound effects audiocards to invite further research in audio language modeling for sound design.

K. Mahesh, S. K. Ranjith, R. Mini

Azrul Azim Abdullah Hashim, Nor Maniha Abdul Ghani, Mohammad Osman Tokhi

This study develops and validates a force feedback control system for automotive pedals utilizing an optimized PID controller using the hybrid Spiral Sine-Cosine algorithm (SSCA). The primary objective is to enhance system performance by integrating SSCA-tuned PID control and comparing results from simulation and Hardware-in-the-Loop (HIL) testing. Auto Regressive with Exogenous inputs (NARX) models were used as the system identification method for nonlinear dynamic system to accurately represent actuator and pedal force relationships. Results demonstrated that the HIL setup significantly improved performance metrics compared to simulations: overshoot decreased, rise time improved, and settling time reduced for various force parameters. The study confirms that SSCA-tuned PID control can be effectively implemented in real-life applications, particularly in force control pedal vehicles, with potential benefits including reduced driver fatigue due to the repetitive actions of pressing and releasing the vehicle pedal. Future research will aim to enhance this approach by integrating vehicle speed control with advanced actuator and pedal force control systems. This integration will ensure smoother and more precise control over vehicle dynamics, improving overall responsiveness and efficiency. Moreover, a primary focus will be on optimizing low-speed driving scenarios, particularly in traffic congestion, where precise control is critical. By addressing challenges such as stop-and-go movement, vehicle jerks, and energy efficiency, this research seeks to enhance both driver comfort and safety in urban traffic conditions.

Huimin Huo, Haiying Bao, Hao Yin

The process of cultivating edible and medicinal mushrooms results in considerable biomass waste, which can be a rich source of bioactive compounds. This research aimed at optimizing the yield of polyphenols and improving the antioxidant potential of extracts derived from the stem waste of Flammulina velutipes (FVS). Notably, this investigation is the first to harness FVS as a raw material for the extraction of total polyphenols by nonionic surfactant-integrated ultrasound-assisted extraction (UAE) techniques. Through the screening of nonionic surfactants, it was determined that Genapol X-080 exhibited the highest efficacy in extracting phenolic constituents. When comparing the total polyphenol yields obtained from FVS via various extraction methods, it was found that UAE yielded significantly more polyphenols compared to both stirring-assisted and heating extraction methods. Furthermore, the sustainability analysis highlighted that the combination of nonionic surfactants with UAE presents a more environmentally friendly option, achieving a score of 0.73 in the Analytical Greenness Calculator. To optimize the extraction parameters, response surface methodology was utilized. The ideal conditions determined were: an ultrasound time of 90 min, a temperature of 44 °C, a 75 % ethanol concentration, and ultrasonic power set at 160 W. Comparative assessments revealed that the refined UAE method enhanced the extraction rate by 27.35 %, while simultaneously decreasing energy use by 66.67 %. Moreover, evaluations of the antioxidant levels in the samples showed that the optimized FVS extracts consistently demonstrated superior antioxidant activities across all evaluated free radical scavenging tests (DPPH, ABTS, FRAP, and hydroxyl radicals). These results provide significant insights into the effective utilization of mushroom stem waste and advance efforts in the valorization of biomass byproducts from the mushroom industry.

Parisa Feizi, Seid Mahdi Jafari

This study aimed at optimizing the extraction of astaxanthin (ASX) by microwave- and ultrasound-assisted methods through an organic solvent (a mixture of ethanol and ethyl acetate), a green solvent (a microemulsion (MEL) of tributyloctylphosphonium bromide ionic liquid in water), and a vegetable oil (sunflower oil) from red (Gracilaria corticate/GC) and brown (Sargassum polycystum/SP) macroalgae. First, MELs were evaluated in terms of density, particle size and conductivity. Then, total carotenoids, ASX content, ASX extraction efficiency and DPPH scavenging activity were measured to evaluate the performance of different extraction methods. According to the results, the density of MEL was 0.97 g/cm3, its diameter was 15.80 nm and the conductivity was 312 µS/cm at 27.1 °C. Based on our findings, the optimal conditions for the extraction of ASX from SP and GC by sonication were the solvent to sample ratio of 20:1, power of 200 W and time of 30 min; for microwave processing, the solvent to sample ratio of 20:1, power of 100 W and time of 5 s. The results showed that in both methods, the ionic MEL solvent showed better performance in terms of ASX extraction, and SP resulted in more total carotenoids compared to GC. Microwave treatment was more effective for extracting ASX from brown algae due to its ability to rapidly disrupt cells from the inside, improve solute–solvent interactions, and overcome structural resistance of algal tissues more efficiently than sonication. Based on the results, red (GC) and brown (SP) macroalgae can be suggested as accessible and inexpensive sources for extracting valuable compounds such as bioactives and the natural pigment astaxanthin.

Jun Lan, Yumin Zhou, Xin Bu et al.

The challenge of achieving effective sound insulation using metamaterials persists in the field. In this research endeavor, a novel three-layer membrane-type acoustic metamaterial is introduced as a potential solution. Through the application of orthogonal experiments, remarkable sound insulation capabilities are demonstrated within the frequency spectrum of 100-1200 Hz. The sound insulation principle of membrane-type acoustic metamaterial is obtained through the analysis of eigenmodes at the peak and trough points, combined with sound transmission loss. In addition, an orthogonal experiment is utilized to pinpoint the critical factors that impact sound insulation performance. By using relative bandwidth as the classification criterion, the optimal combination of influencing factors is determined, thereby improving the sound transmission loss of the multi-layer membrane-type acoustic metamaterial structure and broadening the sound insulation bandwidth. This study not only contributes a fresh and practical approach to insulation material design but also offers valuable insights into advancing sound insulation technology.

Dongzhe Zhang, Jianfeng Chen, Jisheng Bai et al.

Deep learning-based sound event localization and classification is an emerging research area within wireless acoustic sensor networks. However, current methods for sound event localization and classification typically rely on a single microphone array, making them susceptible to signal attenuation and environmental noise, which limits their monitoring range. Moreover, methods using multiple microphone arrays often focus solely on source localization, neglecting the aspect of sound event classification. In this paper, we propose a deep learning-based method that employs multiple features and attention mechanisms to estimate the location and class of sound source. We introduce a Soundmap feature to capture spatial information across multiple frequency bands. We also use the Gammatone filter to generate acoustic features more suitable for outdoor environments. Furthermore, we integrate attention mechanisms to learn channel-wise relationships and temporal dependencies within the acoustic features. To evaluate our proposed method, we conduct experiments using simulated datasets with different levels of noise and size of monitoring areas, as well as different arrays and source positions. The experimental results demonstrate the superiority of our proposed method over state-of-the-art methods in both sound event classification and sound source localization tasks. And we provide further analysis to explain the reasons for the observed errors.

Mohamed F. Mansour

We describe a new method for estimating the direction of sound in a reverberant environment from basic principles of sound propagation. The method utilizes SNR-adaptive features from time-delay and energy of the directional components after acoustic wave decomposition of the observed sound field to estimate the line-of-sight direction under noisy and reverberant conditions. The effectiveness of the approach is established with measured data of different microphone array configurations under various usage scenarios.

A. Akay

Cheuk-Him Yeung, William J. Parnell, Tom Shearer

An active cloaking strategy for the scalar Helmholtz equation in three dimensions is developed by placing active sources at the vertices of Platonic solids. In each case, a “silent zone” is created interior to the Platonic solid and only the incident field remains in a defined region exterior to this zone. This distribution of sources ensures that implementation of the cloaking strategy is efficient: once the multipole source amplitudes at a single source location are determined, the other amplitudes are calculated by multiplying the multipole source vector by a rotation matrix. The technique is relevant to any scalar wave field.

Poirier-Quinot David, Lawless Martin S.

For augmented reality experiences, users wear head-mounted displays (HMD) while listening to real and virtual sound sources. This paper assesses the impact of wearing an HMD on localization accuracy of real sources. Eighteen blindfolded participants completed a localization task on 32 loudspeakers while wearing either no HMD, a bulky visor HMD, or a glass visor HMD. Results demonstrate that the HMDs had a significantly impact on participants’ localization performance, increasing local great circle angle error by 0.9°, and that the glass visor HMD demonstrably increased the rate of up–down confusions in the responses by 0.9–1.1%. These results suggest that wearing an HMD has a sufficiently small impact on real source localization that it can safely be considered as an HMD-free condition in most but the most demanding AR auditory localization studies.

Yiqiang Cai, Peihong Zhang, Shengchen Li

Recent studies focus on developing efficient systems for acoustic scene classification (ASC) using convolutional neural networks (CNNs), which typically consist of consecutive kernels. This paper highlights the benefits of using separate kernels as a more powerful and efficient design approach in ASC tasks. Inspired by the time-frequency nature of audio signals, we propose TF-SepNet, a CNN architecture that separates the feature processing along the time and frequency dimensions. Features resulted from the separate paths are then merged by channels and directly forwarded to the classifier. Instead of the conventional two dimensional (2D) kernel, TF-SepNet incorporates one dimensional (1D) kernels to reduce the computational costs. Experiments have been conducted using the TAU Urban Acoustic Scene 2022 Mobile development dataset. The results show that TF-SepNet outperforms similar state-of-the-arts that use consecutive kernels. A further investigation reveals that the separate kernels lead to a larger effective receptive field (ERF), which enables TF-SepNet to capture more time-frequency features.

Maxime Poret, Catherine Semal, Myriam Desainte-Catherine

Based on a case study on 3D printing, we have been experimenting on the sonification of multidimensional data for peripheral process monitoring. In a previous paper, we tested the effectiveness of a soundscape which combined intentionally incongruous natural and musical sounds. This was based on the hypothesis that auditory stimuli could better stand out from one another if they were less ecologically coherent, thus allowing for better reaction rates to various notifications. In this paper, we follow up on that hypothesis by testing two new acoustic ecologies, each exclusively consisting of either musical or natural sounds. We then run those ecologies through the same dual-task evaluation process as the previous one in order to compare them. The results seem to favor our hypothesis, as the new ecologies were not detected as accurately as the original. Though, the set of natural sounds seemed to be considered less intrusive by testers, and to allow for a better performance at an external primary task. We hope to see this work become part of a much larger corpus of studies, which may eventually provide a more definite answer on the effect of ecological coherence in peripheral soundscape design.

Samuel Clarke, Ruohan Gao, Mason Wang et al.

Objects make unique sounds under different perturbations, environment conditions, and poses relative to the listener. While prior works have modeled impact sounds and sound propagation in simulation, we lack a standard dataset of impact sound fields of real objects for audio-visual learning and calibration of the sim-to-real gap. We present RealImpact, a large-scale dataset of real object impact sounds recorded under controlled conditions. RealImpact contains 150,000 recordings of impact sounds of 50 everyday objects with detailed annotations, including their impact locations, microphone locations, contact force profiles, material labels, and RGBD images. We make preliminary attempts to use our dataset as a reference to current simulation methods for estimating object impact sounds that match the real world. Moreover, we demonstrate the usefulness of our dataset as a testbed for acoustic and audio-visual learning via the evaluation of two benchmark tasks, including listener location classification and visual acoustic matching.

M. Crocker

Biberger Thomas, Ewert Stephan D.

Auditory perception involves cues in the monaural auditory pathways, as well as binaural cues based on interaural differences. So far, auditory models have often focused on either monaural or binaural experiments in isolation. Although binaural models typically build upon stages of (existing) monaural models, only a few attempts have been made to extend a monaural model by a binaural stage using a unified decision stage for monaural and binaural cues. A typical prototype of binaural processing has been the classical equalization-cancelation mechanism, which either involves signal-adaptive delays and provides a single channel output, or can be implemented with tapped delays providing a high-dimensional multichannel output. This contribution extends the (monaural) generalized envelope power spectrum model by a non-adaptive binaural stage with only a few, fixed output channels. The binaural stage resembles features of physiologically motivated hemispheric binaural processing, as simplified signal-processing stages, yielding a 5-channel monaural and binaural matrix feature “decoder” (BMFD). The back end of the existing monaural model is applied to the BMFD output and calculates short-time envelope power and power features. The resulting model accounts for several published psychoacoustic and speech-intelligibility experiments and achieves a prediction performance comparable to existing state-of-the-art models with more complex binaural processing.