Archives of Acoustics

The present study focuses on the spatial characteristics of the sound pressure level (SPL) generated by a circular piston (a circular-shaped acoustic transducer or loudspeaker). It presents a short theoretical review to aid inunderstanding the primary sound field characteristic – acoustic pressure – as a function of time, frequency, directivity angle, and distance from the source. The study introduces a simple and practical criterion for determining the near- and far-field boundary along the axis of the circular piston as a function of frequency. This criterion is validated through theoretical analysis and experimental measurements. Overall, the results show the influence of circular piston parameters on the SPL spatial distribution.

This paper aims to determine the equivalent static elastic constant of a cello’s top plate in the interaction with the bridge. Experimental results detailing this constant are presented based on measuring the deformation and forces caused by a system of calibrated springs in similar conditions to that obtained when these forces are produced by the action of the strings. Subsequent tests are conducted following an intervention by a luthier to adjust the sound post, with the aim of assessing the impact on the elastic constants.

In recent years, single vector hydrophones have attracted widespread attention in target direction estimation due to their compact design and advantages in complex underwater acoustic environments. However, traditional direction of arrival (DOA) estimation algorithms often struggle to maintain high accuracy in nonstationary noise conditions. This study proposes the novel DOA estimation method based on a convolutional neural network (CNN) and the convolutional block attention module (CBAM). By inputting the covariance matrix of the received signal into the neural network and integrating the CBAM module, this method enhances the model’s sensitivity to critical features. The CBAM module leverages channel and spatial attention mechanisms to adaptively focus on essential information, effectively suppressing noise interference and improving directional accuracy. Specifically, CBAM improves the model’s focus on subtle directional cues in noisy environments, suppressing irrelevant interference while amplifying essential signal components, which is crucial for an accurate DOA estimation. Experimental results under various signal-to-noise ratio (SNR) conditions validate the method’s effectiveness, demonstrating superior noise resistance and estimation precision, providing a robust and efficient solution for underwater acoustic target localization.

This article explores the challenge of identifying noise-generating factors in traffic flows (TFs) within the constrained spaces and imperfect transport networks of historical cities, using Lviv as a case study. Experimental studies were conducted to measure the equivalent noise levels at different times of the day on selected streets in Lviv. These streets are characterized by dense development, paved surfaces, and a high volume of vehicular and rail traffic. The study identified correlations between noise levels, traffic volumes, and vehicle speeds during daytime and nighttime periods. Notably, vehicle speed was found to have a more significant impact on noise levels than the number of vehicles. Through the analysis of these findings, empirical mathematical models were developed and validated using the Lagrange interpolation polynomial to predict noise pollution levels on selected streets at specific times. The developed computer system enables quick forecasting of noise levels for a given street while simultaneously provides data to manage TF as a factor affecting noise generation. Crucially, this tool can also assist in calculating the required specifications for acoustic insulation on building fa¸cades adjacent to these TFs.

Good speech intelligibility in university classrooms is crucial to the learning process, ensuring that students can clearly hear all conversations taking place in the classroom. While it is well known that speech intelligibility depends on the geometrical characteristics of a space and the properties of its surfaces, other factors need also to be considered. Among the most important are: the heating, ventilation, and air conditioning (HVAC) systems used in classrooms. Fan noise from HVAC systems increases the background noise level (BNL), negatively affecting speech intelligibility. In addition, the movement of air caused by these systems alters room acoustic variables. Although this dynamic situation is often overlooked in the early design stages, HVAC systems are often active during lectures and influence acoustics variables, especially the speech transmission index (STI). In this study, the impact of HVAC systems on the STI was measured in five different unoccupied classrooms in the Rafet Kayı¸s Faculty of Engineering at Alanya Alaaddin Keykubat University. The results were evaluated according to relevant standards. The results of these evaluations offer insights for researchers, architects, and engineers working in the field of acoustics.

Underwater acoustic target classification has become a key area of research for marine vessel classification, where machine learning (ML) models are leveraged to identify targets automatically. The major challenge is inserting area-specific understanding into ML frameworks to extract features that effectively distinguish between different vessel types. In this study, we propose a model that uses the coherently averaged power spectral estimation (CAPSE) algorithm. Vessel frequency spectra is first computed through the CAPSE analysis, capturing key machinery characteristics. Further, the features are processed via a vision transformer (ViT) network. This method enables the model to learn more complex relationships and patterns within the data, thereby improving the classification performance. This is accomplished by using self-attention mechanisms to capture global dependencies between features, enabling the model to focus on relationships throughout the entire input. The results, evaluated on standard DeepShip and ShipsEar datasets, show that the proposed model achieved a classification accuracy of 97.98% and 99.19% while utilizing just 1.90 million parameters, outperforming other models such as ResNet18 and UATR-Transformer in terms of both accuracy and computational efficiency. This work offers an improvement to the development of efficient marine vessel classification systems for underwater acoustics applications, demonstrating that high performance can be achieved with reduced computational complexity.

This paper addresses the detection of divers with an open-circuit scuba. An acoustic vector sensor (AVS), which contains four channels, one for the pressure component, and three for orthogonal particle velocity components is proposed to be used. A novel covariance matrix analysis (CMA) method is presented for estimating the signal power using AVS in three-dimensional measurements. This method is based on solving a quartic equation that relates the determinant and trace of the AVS covariance matrix to the reciprocal of the signalto- noise ratio (SNR) in a three-dimensional isotropic acoustic field with spherical isotropic noise. This method is compared with two traditional methods: the AVS pressure channel power, and the minimum variance distortionless response (MVDR) beamformer, in estimating the acoustic power associated with the diver’s breathing. Experimental data from sea trials demonstrate the capability of all three methods to reconstruct the waveform of the acoustic diver signal and highlight the periodic breathing patterns. The diver’s breathing rate and corresponding power are estimated using the fast Fourier transform (FFT) of the power signal, therefore serving as a key signature for diver detection. The experiment demonstrates that the CMA method gives better diver detection index compared to the other two methods.

Over the past decade, extensive research has been conducted in the field of piezoelectric energy harvesting, which drives advancements in novel designs and techniques. In this study, the vibration of an electric motor is characterized, and a piezoelectric energy harvester (PEH) beam with a natural frequency of 50 Hz is designed and fabricated. Then, the electromechanical characteristics of the PEH are simulated and tested through both finite element simulation and experiment. The validated simulation model can accurately predict the vibration characteristics of the PEH, which can be utilized for design improvements. Based on this beam structure, three sets of PEHs with different sizes are designed. A study of the output voltage and fatigue life of these different PEH sizes is conducted, and the relationship between the electromechanical coupling effect and its varying values is discussed. Based on the results, design schemes 1 ∼6 demonstrate advantages in terms of output voltage efficiency and fatigue strength, making them suitable for various environments and application purposes. This study establishes an efficient method for analyzing the structural parametric performance of piezoelectric cantilever beams, which paves the way for future research on fatigue-based structural design guidelines for PEH in electric motors.

Switching to renewable energy has been accelerated in recent decades due to the depleting fossil fuel reserves and the need to mitigate environmental and climate degradation. Wind power, especially in urban areas, has seen a significant growth. A critical consideration in the urban wind turbine installation is the noise impact on residents. This study investigates the noise generated by wind turbines under different operational conditions, comparing single-segment and five-segment rotor designs. Various acoustic analyses were conducted, including broadband analysis with weighting curves Z, A, C, and G, a narrowband analysis using 1/12 octave bands, and broadband calculations of sound quality indicators such as sharpness, roughness, and fluctuation strength (FS). The FS was also examined in the Bark scale frequency domain. The study linked the acoustic analysis with the rotor efficiency related to power production. The findings indicate that five-segment rotors generate less acoustic energy due to phase shifts, enhancing dissipation rates, and acoustic energy decreases with the increasing load, peaking when rotors are free at high revolutions per minute (RPM). While single-segment rotors show higher efficiency, they produce more noise. In contrast, five-segment rotors offer a better sound quality, making them preferable despite a lower efficiency. This research provides essential insights into designing urban wind turbines that balance efficiency and noise, crucial for sustainable energy solutions.

Bird sounds collected in the field usually include multiple birds of different species vocalizing at the same time, and the overlapping bird sounds pose challenges for species recognition. Extracting effective acoustic features is critical to multi-label bird species classification task. This work has extended an efficient transfer learning technique for labelling and classifying multiple bird species from audio recordings, further laying the foundation for conservation plans. A synthetic dataset was created by randomly mixing original single-species bird audio recordings from the Cornell Macaulay Library. The final dataset consists of 28 000 audio clips, each 5 s long, containing overlapping vocalizations of two or three bird species among 11 different species. Several pre-trained convolutional neural networks (CNNs), including InceptionV3, ResNet50, VGG16, and VGG19, were evaluated for extracting deep features from audio signals represented as mel spectrograms. The long short-term memory network (LSTM) was further employed to extract temporal features. A multi-label bird species classification was investigated. The absolute matching rate, accuracy, recall, precision, and F1-score of the InceptionV3+LSTM model for multi-label bird species classification are 98.25 %, 99.32 %, 99.41 %, 99.90 %, and 99.57 %, respectively, with the minimum Hamming loss of 0.0062. The results show that the proposed method has excellent performance and can be used for multi-label bird species classification.

The difference in sound pressure levels between two types of rounds fired from a saluting gun has been investigated; the rounds being identified as ‘current’ and ‘new’. A 3-pounder saluting gun mounted on a concrete floor based at HMNB Portsmouth, UK, was used in the survey. Sound pressure levels were measured at the two people responsible for operating the gun: the firer and the loader. Twelve current rounds and 24 new rounds were fired during the survey. The new rounds showed a greater variation in peak sound pressure levels between rounds (interquartile range of 2.1 dB, firer’s location) compared with the current rounds (interquartile range of 1.1 dB, firer’s location). The highest C-weighted peak sound pressure levels for the firer were 173.1 dB for the current round compared with 166.8 dB for the new round. The corresponding highest C-weighted peak sound pressure levels for the loader were 170.6 dB and 163.0 dB, respectively. The difference between median peak sound pressure levels was 8.8 dB for the firer and 9.8 dB for the loader. Similar differences were measured in sound exposure levels between the two types of rounds. Frequency data presented can be used for assessing the suitability of appropriate hearing protectors. Mitigation measures are proposed for further reducing noise exposure of the operators.

To predict the scattered acoustic field for underwater targets with separate transmission and reception points, a forecasting method based on limited scattered acoustic pressure data is proposed. This method represents the scattered acoustic field as the product of an acoustic scattering transfer function and a source density function. By performing numerical integration, the transfer function is obtained using the model surface grid information as input. An equation system concerning the unknown source density function is then derived using the computed scattering transfer matrix, the principle of acoustic reciprocity, and the geometric properties of the target. The unknown source density function is solved using the least squares method. The scattered field with separate transmission and reception points is then obtained by multiplying the calculated transfer matrix with the estimated source density function. This paper applies the finite element method (FEM) to solve the scattering field for a benchmark model with separate transmission and reception points. Using a subset of the elements as input, predictions of the omnidirectional scattered field were made. The predicted results were subsequently compared with those obtained from FEM simulations. The simulation results demonstrate that the proposed method maintains high computational accuracy and is applicable to the prediction of low-frequency scattered fields from underwater targets with spatially separated source and receiver. Further comparison with the FEM-calculated target strength patterns across varying incident–reception angles reveals a high level of agreement, indicating that accurate bistatic target strength predictions can be achieved with a limited amount of input data.