Acoustics Australia最新文献

The cat family Felidae is one of the most successful carnivore lineages today. However, the study of acoustic communication between felids remains a challenge due to the lack of fossils, the limited availability of audio recordings because of their largely solitary and secretive behaviour, and the underdevelopment of computational models and methods needed to address these questions. This study attempts to develop a machine learning-based approach which can be used to identify acoustic features that distinguish felid call types and species from one another through the optimization of classification tasks on these call types and species. A felid call dataset was developed by extracting audio clips from diverse sources. Due to the limited availability of samples, this study focused on the Pantherinae subfamily. The audio clips were manually annotated for call type and species. Time–frequency features were then extracted from the dataset. Finally, several multi-class classification algorithms were applied to the resulting data for classifying species and call types. We found that duration, mean mel spectrogram, frequency range, and amplitude range were among the most distinguishing features for the classifications.

The sound absorption coefficient (SAC) of a composite multi-cell sound absorber in the low- and mid-frequency range is investigated by using experiment and numerical method. The composite sound absorber includes a MPP (micro-perforated panel) layer, a porous material layer, and an air cavity layer. The sandwich acoustic structure consists of an air cavity layer in between two MPP layers, which is backed by another air cavity layer. Maa’s model was used to describe the MPP layer, and the porous material layer was established by using Delany and Bazley’s model. The transfer matrix method (TMM) was used to calculate the surface impedance of each acoustic unit-cell of the composite multi-cell sound absorber, and the SAC of the composite multi-cell sound absorber was predicted by using the equivalent circuit method. Finite element (FE) models of the composite multi-cell sound absorbers are presented, and their sound absorption coefficient was measured by using an impedance tube method. The measurement data demonstrate the validity of the prediction results and are used to analyse various acoustic characteristics that depend on the structural parameters of each acoustic unit-cell. Furthermore, an optimal combination of the structural parameters of the composite multi-cell sound absorber can be realized by using the genetic algorithm (GA). The effect of the number of the acoustic unit-cells with different perforation ratios of the MPP layer and the depth of the air cavity layer is presented. They are the main design parameters that can control the SAC in different frequency ranges. The results also show that the SAC of the composite multi-cell sound absorber can be adjusted by increasing the number of the acoustic unit-cells and using the optimized design of the air cavity layer.

Experimental data were measured by Caldersmith over many years and during different stages of the construction of several violins. Unfortunately, it seems that these valuable data were never published. From the personal notes of Caldersmith that were printed (where resonant frequency, mass, thickness, instrument model, etc., are listed), a possible link between resonant frequencies of free plates and their finished instruments was investigated. However, there is no evidence to support that such a link could be strong; thus the rigidity of the rib assembly of the violins may play a more important role than generally considered.

This paper aims to study the current legislation in the Arab world regarding noise pollution and to compare its laws, in a second step, with those of the European Union to identify the differences and similarities between these two groups of countries' policies. The methodology of this study was based mainly on analytical and comparative approaches to process the data collected from 31 legislation texts from 22 Arab countries. The data were obtained primarily from the official and governmental websites of the various ministries and state institutions concerned with environmental laws, using Arabic and second-language web searches in each country. This study shows that legislators in the investigated countries are relatively engaged in addressing environmental issues, especially noise pollution. Except for Somalia, all Arab League member countries have developed laws and regulations on noise pollution. However, there are no guidelines on noise measurement except in Oman, Qatar, and Saudi Arabia. As well, the governments of these countries still need to develop guidelines for noise mapping. Therefore, it is believed that the legislation in these countries needs to be revised and stronger. In addition, no trace of the soundscape subject has been highlighted in the Arabic legislative texts. The comparative analysis indicates that most Arab laws do not comply with the international standards established by the European Union governments. According to this study, revising and updating Arab laws is necessary, and action strategies must be implemented to achieve sustainability goals.

Efficient and accurate prediction of ocean ambient noise spectrum level is very important to improve the detection capability of sonar equipment. The more factors taken into account, the more complex the establishment and application of the prediction model, which makes a low prediction efficiency. As a data-driven technology, neural networks have the ability to accurately predict the state of complex systems and can avoid complex physical modeling. In this study, a neural network model is built to predict ocean ambient noise spectrum level based on the data of sea water depth, temperature, salinity, sea surface wind speed and rainfall. The model is based on Genetic Algorithm (GA), Levenberg–Marquardt algorithm (LM) and Back Propagation (BP) neural network. The use of GA and LM makes the model combine the powerful mapping ability of neural network and the global search characteristic of GA. The model is used to predict the variation characteristics of spectral levels with frequency, depth, wind speed and rainfall rate, respectively. The predicted values are compared with the real values, for example, the RMSE values are all nearly below 2.04. The results show that the GA-LM-BP neural network prediction model is accurate and effective, and has flexible input factor scalability, which provides a paradigm framework for the establishment of multi-source and multi-factor spectral level prediction model of ocean ambient noise spectrum level based on deep learning.

Tyre cavity resonance noise is one kind of low-frequency and narrow-band noise that particularly affects the passengers inside the cabin of vehicle, especially when driving at a medium speed. In this paper, a noise reduction structure made of multiple resonators is proposed to reduce this type of noise. Based on the local resonance principle, the dimension of the resonator unit is determined by the tyre cavity resonance frequency. In order to obtain this characteristic frequency and the acoustic feature, the acoustic-structure coupling model of the tyre and cavity is established by the finite element method (FEM), and the modal frequency and shape of the tyre cavity are calculated and validated by the experimental results. Based on these analyses, the geometric and material parameters of the sound reduction structure are calculated to match the resonant frequency of the tyre cavity. A long belt filled with multiple resonators is designed to fit the profile of the tyre cavity, and simulations and experimental tests are conducted to investigate the noise reduction performance. The results show that the multiple resonators can significantly reduce the sound pressure inside the tyre cavity due to the vibroacoustic coupling effect. This paper provides a novel solution for reducing tyre cavity resonance noise.

Classrooms are important learning spaces, however, the acoustic conditions in these spaces can often be suboptimal. The aim of this scoping review, which used the PRISMA-ScR protocol, was to understand what is known from the literature about the effect of classroom acoustic treatment on students’ listening, learning, and well-being. Thirteen papers from the database searches were deemed relevant for the review. Information on the years of publication of the papers, the population studied, the types of acoustic treatment used, the measures and methods used to assess the effect of acoustic treatment, and the outcomes of the papers was gathered. Seven of the 13 studies reported positive effects of classroom acoustic treatment on student’s speech perception, attention, and well-being. Five studies reported both positive effects and no effect depending on the measure, condition, or population. The remaining study reported a negative effect of classroom acoustic treatment on children’s speech perception and listening effort. These findings suggest that the effect of ceiling and/or wall absorbers/diffusers on sound and reverberation in the room can help students’ speech perception, attention, reading, and well-being, but they may also reduce the speech transmission index resulting in increased listening effort. The limitations of the reviewed studies and avenues for future research on the effect of acoustic treatment on a broader range of listening, learning, and well-being outcomes for students are discussed.

Active control of road noise has been widely recognized as a promising technology. For a feed-forward active road noise control system, the proper selection of the reference vibration signals is very important. It determines the theoretically optimal effect that the system can achieve. In order to achieve a reasonable selection of reference signals, a modified signal selection method is proposed in this paper. A test on road noise data acquisition and relevant analysis is conducted to decide the target frequency band for noise control. After that, an initial reference signal set is established and it is expanded by iteratively calculating the Fisher information matrix and the amount of new correlation information so that a reference signal set that has optimal multiple coherence with road noise is obtained. In the case study, a comparison between the traditional method and the proposed method is conducted. The results show that the proposed method can reach a higher average coherence coefficient and better noise reduction performance than the traditional method.