Archives of Acoustics

This study explores the localization of virtual sound sources reproduced by a crosstalk cancellation system under different reflective conditions in virtual rooms, analyzing the results with binaural cues. Binaural room impulse responses were generated using the high-order image source method. By modifying the acoustic parameters of the virtual room to manipulate reflection intensity and temporal structure, psychoacoustic experiments were conducted using headphone reproduction. Results show that variations in reflection intensity within a certain range, achieved by altering the room reverberation time (RT), do not significantly affect virtual source localization. However, increasing the loudspeaker–listener distance (altering the temporal structure of reflections) deteriorates localization performance. The main difference between changes in loudspeaker–listener distance and RT lies in whether the reflection’s temporal structure changes. The study highlights the critical role of reflection temporal structure in virtual source localization. Binaural cue analysis shows that even in reverberant environments, interaural time difference (ITD) remains more consistent with localization accuracy than interaural level difference (ILD).

When evaluating speech intelligibility (SI) in automotive cabins, binaural measurements typically employ a fixed dummy head. However, the impact of listener head positions on SI in nonuniform cabin sound fields remains unclear. This study analyzed SI under various listener head positions in an automotive cabin. An artificial mouth was regarded as the speaker, which was placed in three passenger positions. Binaural room impulse responses were measured using a dummy head in the driver’s seat with various head positions. The results show that head position significantly affects SI, with variations of up to 7 dB in octave band magnitudes, more than one just-noticeable difference in the speech transmission index, and shifts of up to 2.5 dB in the speech-reception threshold. SI variability depends on the speaker’s location. Directivity patterns play a crucial role in the front-passenger position, while seat occlusion affects SI at the back-right position, causing substantial decreases below a certain height threshold. At the back-left position, head positions close to the headrest enhance SI due to distance and reflections. Minor head displacements (4 cm apart) generally have insignificant effects on SI, except near seat obstructions or reach critical thresholds.

While acoustic vector sensors (AVS) are well-established for detection and direction-of-arrival (DOA) estimation using co-located pressure and particle motion (PM) measurements, their potential for passive range estimation remains largely unexplored. This paper introduces a novel single-AVS method for passive range estimation to an acoustic monopole source by exploiting the fundamental near-field dominance of PM energy. We derive the frequency and distance dependent ratio (ξ) of kinetic to potential acoustic energy density – a key near-field signature inaccessible to conventional hydrophones. By leveraging simultaneous AVS pressure and PM velocity measurements, our method estimates ξ, inverts the monopole near-field model to obtain the Helmholtz number, and directly computes the range. Crucially, we demonstrate that PM sensors offer a potential signal-to-noise ratio (SNR) advantage over pressure sensors within the near-field (>7.8 dB). Validation under simulated noise conditions shows accurate range estimation (RMSE <10%) for low-frequency sources (<100 Hz) within 8–25 m ranges at 0 dB SNRs, with performance degrading as frequency increases or SNR decreases. Critically, robustness is confirmed using recorded basin noise profiles, overcoming the isotropic Gaussian noise assumption. This technique extends AVS functionality beyond DOA, enabling single-sensor passive ranging without arrays, environmental priors, or reference signals where conventional methods fail.

Microphones are sensors common to a variety of the Internet of Things (IoT) and healthcare applications. Many examples have proved that microphones can be useful in detecting, e.g., abnormal breathing rates. There are already applications that serve this purpose, such as respiratory acoustic monitoring and ResApp. Breath signals have been studied using a range of technologies and sensors, including the most common: radar, accelerometer, and wearables. The majority of these sensors are attached to the body of a monitored person. However, the emergence of COVID-19 has drawn particular attention to the importance of using non-contact technologies for monitoring breath signals and other vital signs. This paper presents a comprehensive review of microphone-based non-contact vital sign monitoring, including the methodologies and concepts, while identifying new research gaps and opportunities for future studies.

This article compares two methods for determining the air flow resistivity of porous and coating materials – a key parameter in sound absorption modelling. The analysis involves a modified static airflow measurement procedure in accordance with International Organization for Standardization (ISO) (2018), using a linear approximation algorithm (PLA), and a reverse method consisting of matching the measured absorption coefficient in an impedance tube to the Miki model. The analysis was conducted on both porous materials utilised in acoustic panel fillings and thin coverings. It is evident that both methods yield analogous outcomes for materials exhibiting low resistivity. However, for materials characterised by higher resistivity, discrepancies of up to 50% were observed. Nevertheless, a high degree of agreement was obtained between the calculated and measured absorption coefficients. For thin coating materials, an air gap of at least 70 mm is required. For materials with a thickness of up to approximately 30 mm, differences in resistivity do not significantly affect the absorption coefficient. It is evident that both methods can be used to determine the air flow resistivity of porous materials and layered structures, supporting the effective selection of materials according to requirements.

Metamaterials with Fabry–Pérot (FP) resonance have proven effective for underwater ultrasound imaging. The propagation phenomenon can be understood as a spatial filter with linear dispersion over a finite bandwidth. However, conventional imaging techniques are constrained by the diffraction limit or rely on a strong impedance mismatch between the metamaterial and water. In this paper, we propose a columnar array metamaterial designed for underwater imaging based on FP resonances and validate the proposed design through numerical simulations. The acoustic pressure transmission coefficient, together with the normalized acoustic pressure distribution, is analyzed to quantitatively evaluate imaging quality and verify the physical effectiveness of the model. This novel structure enables deep subwavelength imaging underwater, maintaining excellent and stable imaging performance within a 0.4 kHz bandwidth centered around the operating frequency. We use air-filled metamaterials to create strong acoustic coupling and establish effective sound isolation. This approach significantly enhances imaging resolution, while optimizing energy loss at multiple interfaces, an issue in previous studies. Additionally, in contrast to resonance- or refraction-based approaches such as Helmholtz resonators or hyperlens designs, the proposed FP-resonant metamaterial offers an alternative mechanism for achieving near-field subwavelength imaging through controlled wave transmission and confinement. We also examine the influence of various parameters, such as imaging distance, incidence distance, and array periodicity, on imaging performance. The results demonstrate that the columnar array metamaterial holds great potential for underwater ultrasound imaging applications.

In the deep-water reliable acoustic path (RAP), when estimating target depth using a vertical array, a large-aperture array can enhance the extraction of the acoustic field interference structure under low signal-to-noise ratio (SNR). However, this operation introduces slow envelope modulation (the envelope amplitude of peak beam intensity decreases with frequency) to the broadband acoustic field interference pattern, significantly degrading the performance of estimating the source depth. The Kraken normal-mode model can accurately calculate low-frequency sound fields in deep-water environments. This paper uses this tool to find that, in the deep-water direct arrival zone (DAZ), the peak beam intensity output of a vertical linear array varies across a broadband frequency range, exhibiting a pattern combining periodic changes of Lloyd’s mirror interference and inherent envelope attenuation changes. The physical mechanism of envelope attenuation is explained through both theoretical derivation and simulation analysis, key factors affecting the envelope-attenuation pattern are clarified, and the impact of beam-intensity envelope attenuation on the depth-estimation method based on matched beam intensity processing (MBIP) is pointed out. Based on this, a modified target depth estimation method of matched beam intensity processing (M-MBIP) that contains an attenuation coefficient is proposed, and its effectiveness is verified through simulated data.

Acoustic resonators are useful for damping low frequencies. In cylindrical silencers (mufflers), the implementation of the resonance concept consists in selecting such a length of the expansion chamber (EC) that a wave of opposite phase is created in it, and with this opposite phase the incident wave is damped. Based on the plane wave theory (1D) and simple analytical calculations, it is possible to approximately determine the shortest length of the EC for a selected frequency; such a chamber represents the simplest silencer. Its efficiency is measured by the transmission loss (TL) value; increasing the TL value indicates that the silencer efficiency increases as well. The efficiency was improved in two ways: first, in single EC, by adding inlet, outlet, or both horizontal extensions, and second, by adding another EC. In the first case, the influence of the length of the horizontal extensions on TL was analyzed. In the second study, another dedicated EC was added, and the influence of the width and orifice diameter of the transverse partition on TL was analyzed. All analytical results were confirmed experimentally. The results indicate that, first of all, a simple silencer (single EC) is found to damp a dedicated frequency. In addition, simple changes in the structure of such a silencer significantly increase its efficiency.

This article addresses the issue of detecting speech signal segments in an acoustic signal and analyzes potential decision fusion for a group of voice activity detectors (VADs). We designed ten new VADs using three different types of neural network architectures and three time-frequency signal representations. One of the proposed models has higher classification efficiency than competitive solutions. We used our VAD models to analyse data fusion and improve the final classification decision. For this purpose, we used gradient-free and gradient-based optimizers with different objective functions. The analysis revealed the impact of individual classifiers on the final decisions and the potential gains or losses resulting from VAD fusion. Compared with existing models, the models we proposed achieved higher classification accuracy at the cost of increased memory requirements. The final choice of a specific model depends on the platform constraints on which the VAD system will be deployed.