Hasil untuk "Acoustics. Sound"

Qiqing Chen, Lin Xie, Yangcheng Yao et al.

Sonodynamic therapy (SDT) faces significant challenges in treating aggressive malignancies due to inherent apoptotic resistance. To address this, we developed a multifunctional nanoliposome designed for mitochondrial targeting and synergistic induction of dual cell death pathways. Co-loaded with the sonosensitizer HMME and an iron-based Fenton catalyst, the nanoparticle exhibits glutathione-responsive disassembly and promotes robust reactive oxygen species generation under ultrasound irradiation. This leads to potent lipid peroxidation and ferroptosis, effectively bypassing conventional resistance mechanisms. The platform further integrates dual-modal magnetic resonance and photoacoustic imaging capabilities, enabling precise tumor delineation and real-time treatment monitoring. Constructed entirely from clinically approved lipid components, our nanoplatform demonstrates excellent biocompatibility and achieves complete tumor regression in murine models without significant systemic toxicity. This work provides a comprehensive theranostic strategy that combines catalytic amplification with multimodal imaging, offering a clinically translatable approach for the precision treatment of therapy-resistant malignancies.

Hiromitsu Emoto, Takayuki Yamada

Woongji Kim, Beomseok Oh, Junsuk Rho et al.

Parametric arrays (PA) offer exceptional directivity and compactness compared to conventional loudspeakers, facilitating various acoustic applications. However, accurate measurement of audio signals generated by PA remains challenging due to spurious ultrasonic sounds arising from microphone nonlinearities. Existing filtering methods, including Helmholtz resonators, phononic crystals, polymer films, and grazing incidence techniques, exhibit practical constraints such as size limitations, fabrication complexity, or insufficient attenuation. To address these issues, we propose and demonstrate a novel acoustic filter based on the design of a half-wavelength resonator. The developed filter exploits the nodal plane in acoustic pressure distribution, effectively minimizing microphone exposure to targeted ultrasonic frequencies. Fabrication via stereolithography (SLA) 3D printing ensures high dimensional accuracy, which is crucial for high-frequency acoustic filters. Finite element method (FEM) simulations guided filter optimization for suppression frequencies at 40 kHz and 60 kHz, achieving high transmission loss (TL) around 60 dB. Experimental validations confirm the filter's superior performance in significantly reducing spurious acoustic signals, as reflected in frequency response, beam pattern, and propagation curve measurements. The proposed filter ensures stable and precise acoustic characterization, independent of measurement distances and incidence angles. This new approach not only improves measurement accuracy but also enhances reliability and reproducibility in parametric array research and development.

Mingfei Chen, Israel D. Gebru, Ishwarya Ananthabhotla et al.

We introduce SoundVista, a method to generate the ambient sound of an arbitrary scene at novel viewpoints. Given a pre-acquired recording of the scene from sparsely distributed microphones, SoundVista can synthesize the sound of that scene from an unseen target viewpoint. The method learns the underlying acoustic transfer function that relates the signals acquired at the distributed microphones to the signal at the target viewpoint, using a limited number of known recordings. Unlike existing works, our method does not require constraints or prior knowledge of sound source details. Moreover, our method efficiently adapts to diverse room layouts, reference microphone configurations and unseen environments. To enable this, we introduce a visual-acoustic binding module that learns visual embeddings linked with local acoustic properties from panoramic RGB and depth data. We first leverage these embeddings to optimize the placement of reference microphones in any given scene. During synthesis, we leverage multiple embeddings extracted from reference locations to get adaptive weights for their contribution, conditioned on target viewpoint. We benchmark the task on both publicly available data and real-world settings. We demonstrate significant improvements over existing methods.

Silvia Arellano, Chunghsin Yeh, Gautam Bhattacharya et al.

The generation of room impulse responses (RIRs) using deep neural networks has attracted growing research interest due to its applications in virtual and augmented reality, audio postproduction, and related fields. Most existing approaches condition generative models on physical descriptions of a room, such as its size, shape, and surface materials. However, this reliance on geometric information limits their usability in scenarios where the room layout is unknown or when perceptual realism (how a space sounds to a listener) is more important than strict physical accuracy. In this study, we propose an alternative strategy: conditioning RIR generation directly on a set of RIR acoustic parameters. These parameters include various measures of reverberation time and direct sound to reverberation ratio, both broadband and bandwise. By specifying how the space should sound instead of how it should look, our method enables more flexible and perceptually driven RIR generation. We explore both autoregressive and non-autoregressive generative models operating in the Descript Audio Codec domain, using either discrete token sequences or continuous embeddings. Specifically, we have selected four models to evaluate: an autoregressive transformer, the MaskGIT model, a flow matching model, and a classifier-based approach. Objective and subjective evaluations are performed to compare these methods with state-of-the-art alternatives. Results show that the proposed models match or outperform state-of-the-art alternatives, with the MaskGIT model achieving the best performance.

Khalid A. Darabkh, Oswa M. Amro, Feras B. Al-Qatanani

The Global Positioning System (GPS) and Received Signal Strength Indicator (RSSI) usage for location provenance often fails in obstructed, noisy, or densely populated urban environments. This study proposes a passive location provenance method that uses the location’s acoustics and the device’s acoustic side channel to address these limitations. With the smartphone’s internal microphone, we can effectively capture the subtle vibrations produced by the capacitors within the voltage-regulating circuit during wireless transmissions. Subsequently, we extract key features from the resulting audio signals. Meanwhile, we record the RSSI values of the WiFi access points received by the smartphone in the exact location of the audio recordings. Our analysis reveals a strong correlation between acoustic features and RSSI values, indicating that passive acoustic emissions can effectively represent the strength of WiFi signals. Hence, the audio recordings can serve as proxies for Radio-Frequency (RF)-based location signals. We propose a location-provenance framework that utilizes sound features alone, particularly the Mel-Frequency Cepstral Coefficients (MFCCs), achieving coarse localization within approximately four kilometers. This method requires no specialized hardware, works in signal-degraded environments, and introduces a previously overlooked privacy concern: that internal device sounds can unintentionally leak spatial information. Our findings highlight a novel passive side-channel with implications for both privacy and security in mobile systems.

Xintong Liu, Chunhua Qiu, Tianlin Wang et al.

Sonic layer depth (SLD) is crucial in ocean acoustics research and profoundly influences sound propagation and Sonar detection. Carrying 90% of oceanic kinetic energy, mesoscale eddies significantly impact the propagation of acoustic energy in the ocean. Recent studies classified mesoscale eddies into normal eddies (warm anticyclonic and cold cyclonic eddies) and abnormal eddies (cold anticyclonic and warm cyclonic eddies). However, the influence of mesoscale eddies, especially abnormal eddies, on SLD remains unclear. Based on satellite altimeter and reanalysis data, we explored the influence of mesoscale eddies on seasonal variations in SLD in the South China Sea. We found that the vertical structures of temperature anomalies within the eddies had a significant impact on the sound speed field. A positive correlation between sonic layer depth anomaly (SLDA) and eddy intensity (absolute value of relative vorticity) was investigated. The SLDA showed significant seasonal variations: during summer (winter), the proportion of negative (positive) SLDA increased. Normal eddies (abnormal eddies) had a more pronounced effect during summer and autumn (spring and winter). Based on mixed-layer heat budget analysis, it was found that the seasonal variation in SLD was primarily induced by air–sea heat fluxes. However, for abnormal eddies, the horizontal advection and vertical convective terms modulated the variations in the SLDA. This study provides additional theoretical support for mesoscale eddy–acoustic coupling models and advances our understanding of the impact of mesoscale eddies on sound propagation.

Dan Zhu, Jiayu Peng, Cong Ding

Airport noise prediction models are divided into physics-guided methods and data-driven methods. The prediction results of physics-guided methods are relatively stable, but their overall prediction accuracy is lower than that of data-driven methods. However, machine learning methods have a relatively high prediction accuracy, but their prediction stability is inferior to physics-guided methods. Therefore, this article integrates the ECAC model, driven by aerodynamics and acoustics principles under the framework of deep neural networks, and establishes a physically guided neural network noise prediction model. This model inherits the stability of physics-guided methods and the high accuracy of data-driven methods. The proposed model outperformed physics-driven and data-driven models regarding prediction accuracy and generalization ability, achieving an average absolute error of 0.98 dBA in predicting the sound exposure level. This success was due to the fusion of physics-based principles with data-driven approaches, providing a more comprehensive understanding of aviation noise prediction.

Ariharasudhan Subramanian, Senthil Kumar Selvaraj, Rajaram Mani et al.

This study investigates the synthesis and characterization of composite materials, pigeon pea stem, and cotton fibers blended in different ratios such as 100/0, 70/30, 60/40, 50/50, 30/70, and 0/100. These composite materials were produced using a compression molding technique. According to ASTM standards, the acoustics, thermal conductivity, and physical characteristics of the composite samples were tested to assess their qualities. The impedance tube method detailed in ASTM E1050 was used to determine the sound absorption coefficients (SAC) for acoustics. The SAC values were measured at six frequencies such as 125, 250, 500, 1000, 2000, and 4000 Hz. The results showed that composite samples made from waste cotton and pigeon pea demonstrated sound absorption values of greater than 80%. Superior sound insulation and absorption, moisture absorption, fiber properties have also been demonstrated by waste composites. Especially, the waste cotton/pea stalk waste fiber composites achieved over 75% sound absorption, while the waste 28% composites performed well in terms of sound absorption, moisture absorption, and fiber properties. Even in humid conditions, the composite samples constructed from used cotton and pigeon pea stalks demonstrated good moisture resistance without reducing their insulating qualities. Soundproofing barriers are composite layers of foam or pigeon pea/cotton.

Maximo Cobos, Mirco Pezzoli, Fabio Antonacci et al.

Acoustic signal processing in the spherical harmonics domain (SHD) is an active research area that exploits the signals acquired by higher order microphone arrays. A very important task is that concerning the localization of active sound sources. In this paper, we propose a simple yet effective method to localize prominent acoustic sources in adverse acoustic scenarios. By using a proper normalization and arrangement of the estimated spherical harmonic coefficients, we exploit low-rank approximations to estimate the far field modal directional pattern of the dominant source at each time-frame. The experiments confirm the validity of the proposed approach, with superior performance compared to other recent SHD-based approaches.

Jörg Rychen, Patrick Paitz, Pascal Edme et al.

Whales and dolphins rely on sound for navigation and communication, making them an intriguing subject for studying language evolution. Traditional hydrophone arrays have been used to record their acoustic behavior, but optical fibers have emerged as a promising alternative. This study explores the use of distributed acoustic sensing (DAS), a technique that detects local stress in optical fibers, for underwater sound recording. An experiment was conducted in Lake Zurich, where a fiber-optic cable and a self-made hydrophone array were deployed. A test signal was broadcasted at various locations, and the resulting data was synchronized and consolidated into files. Analysis revealed distinct frequency responses in the DAS channels and provided insights into sound propagation in the lake. Challenges related to cable sensitivity, sample rate, and broadcast fidelity were identified. This dataset serves as a valuable resource for advancing acoustic sensing techniques in underwater environments, especially for studying marine mammal vocal behavior.

Julia Wilkins, Justin Salamon, Magdalena Fuentes et al.

Finding the right sound effects (SFX) to match moments in a video is a difficult and time-consuming task, and relies heavily on the quality and completeness of text metadata. Retrieving high-quality (HQ) SFX using a video frame directly as the query is an attractive alternative, removing the reliance on text metadata and providing a low barrier to entry for non-experts. Due to the lack of HQ audio-visual training data, previous work on audio-visual retrieval relies on YouTube (in-the-wild) videos of varied quality for training, where the audio is often noisy and the video of amateur quality. As such it is unclear whether these systems would generalize to the task of matching HQ audio to production-quality video. To address this, we propose a multimodal framework for recommending HQ SFX given a video frame by (1) leveraging large language models and foundational vision-language models to bridge HQ audio and video to create audio-visual pairs, resulting in a highly scalable automatic audio-visual data curation pipeline; and (2) using pre-trained audio and visual encoders to train a contrastive learning-based retrieval system. We show that our system, trained using our automatic data curation pipeline, significantly outperforms baselines trained on in-the-wild data on the task of HQ SFX retrieval for video. Furthermore, while the baselines fail to generalize to this task, our system generalizes well from clean to in-the-wild data, outperforming the baselines on a dataset of YouTube videos despite only being trained on the HQ audio-visual pairs. A user study confirms that people prefer SFX retrieved by our system over the baseline 67% of the time both for HQ and in-the-wild data. Finally, we present ablations to determine the impact of model and data pipeline design choices on downstream retrieval performance. Please visit our project website to listen to and view our SFX retrieval results.

Mathias Lemke, Lewin Stein

The identification of sound sources is a common problem in acoustics. Different parameters are sought, among these are signal and position of the sources. We present an adjoint-based approach for sound source identification, which employs computational aeroacoustic techniques. Two different applications are presented as a proof-of-concept: optimization of a sound reinforcement setup and the localization of (moving) sound sources.

Ferrari Cesare Lupo, Cheer Jordan, Mautone Mario

Today’s cars must meet ever-higher acoustic standards, and so, to avoid compromising vehicle dynamics, handling performance and fuel consumption, standard passive methods alone do not provide sufficient performance. Active control solutions can provide a potential solution to this challenge, particularly at low frequency and such systems have been investigated for application to small cars, SUVs and luxury vehicles. These vehicles are generally characterised by fairly slow dynamics and limited noise emission and, therefore, this paper explores the challenging application of active noise control to a two-seat super sports car equipped with a naturally aspirated engine. This work aims to track and then control sounds characterised by extremely rapid frequency variation rates, up to peaks of over 80 Hz/s, and high sound pressure levels. A multi-channel, multi-order FxLMS based control system has been implemented, which has been modified to optimise performance for this application by including both convergence gain and leakage scheduling, to achieve effective control at the driver’s and passenger’s ears. To evaluate the performance of the controller, its performance has been simulated when applied to measurements taken under several vehicle manoeuvres, ranging from conventional constant engine speed to very fast engine run-ups. From the presented results, it is shown that the system can obtain high levels of control during the manoeuvre set, with the controller reducing the overall sound pressure level by more than 10 dB at certain frequencies when analysing a single order, and it reduces the overall loudness by around 5% in all of the analysed cases.

Holger Waubke, Christian H Kasess

Previously, it was shown how the behavior of chain-like structures including hysteretic elements as described by the Bouc model under white noise excitation can be calculated using Gaussian closure. The method results in analytic expressions for the temporal evolution of the statistical moments. Using the example of a liquid-filled tank, the Gaussian closure is generalized to the case of a filtered white noise with a slowly varying intensity as may occur during earthquakes. The complexity of the model is further enhanced for a case that lacks an elastic restoring force at the hysteretic node that couples the tank model with the surface. The new tank model comprises three mechanical degrees of freedom. The first degree of freedom is the movement of mass of the tank. The second degree of freedom is the movement of the swapping mass of the fluid in the tank. The third degree of freedom is the movement of the impulsive mass that is coupled more stiffly.

Jing Wang

The technology for multiple-noise removal has triggered skyrocketing interest in both mathematics and engineering, and the tropical algebra has laid the foundation for an abundance of noise filters. However, the denoising of the filter based on the traditional algebra is inextricably complex, and its algorithm is extremely intricate and awfully inefficient, so it is necessary to estimate the statistical characteristics of noise in a novel way. Now the tropical algebra has opened the path for a new way to design optimally a denoising method, which has obvious advantages over traditional ones in denoising efficiency and simple filtering algorithm. In this paper, the idempotent of the multiplicative semigroup of 4 × 4 tropical matrices is studied. First, the tropical algebra and the tropical matrix multiplicative semigroup are introduced. Second, the idempotent classification of the 4 × 4 tropical matrix multiplicative semigroup is given. Finally, an example is given, in which the filter based on tropical algebra is used for denoising, and an optimization method with tropical polynomials as constraints is given.

Guo Yao, Zhu Hongyan

A random finite set-based sequential Monte–Carlo tracking method is proposed to track multiple acoustic sources in indoor scenarios. The proposed method can improve tracking performance by introducing recognized speaker identities from the received signals. At the front-end, the degenerate unmixing estimation technique (DUET) is employed to separate the mixed signals, and the time delay of arrival (TDOA) is measured. In addition, a criterion to select the reliable microphone pair is designed to quickly obtain accurate speaker identities from the mixed signals, and the Gaussian mixture model universal background model (GMM-UBM) is employed to train the speaker model. In the tracking step, the update of the weight for each particle is derived after introducing the recognized speaker identities, which results in better association between the measurements and sources. Simulation results demonstrate that the proposed method can improve the accuracy of the filter states and discriminate the sources close to each other.

Lam Pham, Dusan Salovic, Anahid Jalali et al.

In this paper, we present a comprehensive analysis of Acoustic Scene Classification (ASC), the task of identifying the scene of an audio recording from its acoustic signature. In particular, we firstly propose an inception-based and low footprint ASC model, referred to as the ASC baseline. The proposed ASC baseline is then compared with benchmark and high-complexity network architectures of MobileNetV1, MobileNetV2, VGG16, VGG19, ResNet50V2, ResNet152V2, DenseNet121, DenseNet201, and Xception. Next, we improve the ASC baseline by proposing a novel deep neural network architecture which leverages residual-inception architectures and multiple kernels. Given the novel residual-inception (NRI) model, we further evaluate the trade off between the model complexity and the model accuracy performance. Finally, we evaluate whether sound events occurring in a sound scene recording can help to improve ASC accuracy, then indicate how a sound scene context is well presented by combining both sound scene and sound event information. We conduct extensive experiments on various ASC datasets, including Crowded Scenes, IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events (DCASE) 2018 Task 1A and 1B, 2019 Task 1A and 1B, 2020 Task 1A, 2021 Task 1A, 2022 Task 1. The experimental results on several different ASC challenges highlight two main achievements; the first is to propose robust, general, and low complexity ASC systems which are suitable for real-life applications on a wide range of edge devices and mobiles; the second is to propose an effective visualization method for comprehensively presenting a sound scene context.

Michele Mancusi, Nicola Zonca, Emanuele Rodolà et al.

The marine ecosystem is changing at an alarming rate, exhibiting biodiversity loss and the migration of tropical species to temperate basins. Monitoring the underwater environments and their inhabitants is of fundamental importance to understand the evolution of these systems and implement safeguard policies. However, assessing and tracking biodiversity is often a complex task, especially in large and uncontrolled environments, such as the oceans. One of the most popular and effective methods for monitoring marine biodiversity is passive acoustics monitoring (PAM), which employs hydrophones to capture underwater sound. Many aquatic animals produce sounds characteristic of their own species; these signals travel efficiently underwater and can be detected even at great distances. Furthermore, modern technologies are becoming more and more convenient and precise, allowing for very accurate and careful data acquisition. To date, audio captured with PAM devices is frequently manually processed by marine biologists and interpreted with traditional signal processing techniques for the detection of animal vocalizations. This is a challenging task, as PAM recordings are often over long periods of time. Moreover, one of the causes of biodiversity loss is sound pollution; in data obtained from regions with loud anthropic noise, it is hard to separate the artificial from the fish sound manually. Nowadays, machine learning and, in particular, deep learning represents the state of the art for processing audio signals. Specifically, sound separation networks are able to identify and separate human voices and musical instruments. In this work, we show that the same techniques can be successfully used to automatically extract fish vocalizations in PAM recordings, opening up the possibility for biodiversity monitoring at a large scale.

Keisuke Yoshioka, Yuta Kurashina, Ami Ogawa et al.

In recent years, individual control of one’s personal environment has been drawing increasing attention due to the growing interest in health care. Wearable devices are especially useful because of their controllability regardless of location. Humidity is one of the inevitable factors in the personal environment as a preventive against infectious diseases. Although atomization devices are commonly used as a method of humidity control, at present, there are no wearable humidity control devices. Vibration of a lithium niobate (LN) device in the thickness mode is a promising piezoelectric method for miniaturization of atomization devices for humidity control. To miniaturize the atomization device, the transducer size needs to be small not so much as to decrease the atomization efficiency. However, the effect of the device area on the atomization efficiency of LN at a size suitable for mounting in wearable devices has not been studied. Here, we conducted an atomization demonstration of LN devices with different sizes to evaluate particle size and atomization efficiency. Furthermore, to reveal the relationship between vibration behavior and atomization efficiency, resonance vibration in the MHz frequency band was evaluated by the finite element method and an impedance analyzer. The results showed that the peak size of water particles atomized by each device was in the range of 3.2 to 4.2 µm, which is smaller than particles produced by typical piezoelectric ceramics. Moreover, the best LN size for efficient atomization was found to be 8 mm × 10 mm among the five LN device sizes used in experiments. From the relationship between vibration behavior and atomization efficiency, the size of the transducer was suggested to affect the vibration mode. The obtained result suggested that the LN device is suitable for small wearable nebulizer devices.