Helmholtz resonator is considered and widely accepted as a basic acoustic model in engineering applications and research. In this paper, the normal incidence sound absorption characteristics of series and parallel configurations of Helmholtz resonators is studied analytically, numerically and experimentally. The proposed analytical model for series configuration of HRs comprises of Johnson–Champoux–Allard model and transfer matrix method while parallel configuration of HRs is described using parallel transfer matrix method. The results from proposed analytical models fit well with the finite element method (FEM) results obtained from COMSOL multiphysics. Incorporation of parallel configuration and proper tuning of geometric parameters helps to overcome the trade-off between broad band sound absorption and minimum space utilization. Also, the experimental observations of one of the parallel configuration substantiates the FEM results. Moreover, the FEM models are more accountable for the variation in neck position and also provide better visualization of acoustic absorption with frequency.

With the huge success of applying deep learning (DL) methodologies to image recognition and natural language processing in recent years, researchers are now keen to use them in the machine condition monitoring (MCM) context. There are numerous papers in applying various DL techniques, such as auto-encoder, restricted Boltzmann machine, convolutional neural network and recurrent neural network, etc., to MCM problems ranging from component-level condition monitoring (machine tool wear prediction, bearing fault diagnosis and classification and hydraulic pump fault diagnosis) to system-level health management (aircraft and spacecraft diagnosis). In this paper, we give a brief overview in the area of DL for MCM with a focus on reviewing the most recent papers published since 2019. In Part 1, we present some critical views regarding whether any breakthrough has been achieved from an MCM domain expert perspective, with the main conclusion that DL has great potential for MCM applications, and a major breakthrough could come soon since the shortfalls lie more in data than in the DL methodologies. Our overall impression is that (a) DL models are not really showing their great potentials with only a small training data; (b) faulty-condition data is hard to come by for training DL, but normal condition data is abundant, so anomaly detection makes more sense; (c) applying DL only to the Case Western Reserve University (CWRU) bearing fault dataset is not sufficient for real world industrial applications as it was from a very simple test rig, and applying DL to data from complex systems like helicopter gearbox data may deliver much more convincing results. In Part 2, we enhance the main conclusion of the critical review with supplement views and a case study on analysing Bell-206B helicopter main gearbox planet bearing failure data using some traditional MCM techniques in contrast to applying the long short-term memory (LSTM) DL method. We can conclude from the case study that the DL-based methods are not necessarily always superior to the traditional MCM techniques for dataset from moderately complex machinery.

Machine health condition monitoring is evidently a crucial challenge nowadays. Unscheduled breakdowns increase operating costs due to repairs and production losses. Scheduled maintenance implies taking the risk of replacing fully operational components. Human expertise is a solution for an outstanding expertise but at a high cost and for a limited quantity of data only, the analysis being time-consuming. Industry 4.0 and digital factory offer many alternatives to human monitoring. Time, cost and skills are the real stakes. The key point is how to automate each part of the process knowing that each one is valuable. Leaving aside scheduled maintenance, this paper copes with condition-based preventive maintenance and focuses on one fundamental step: the signal processing. After a brief overview of this specific area in which numerous technologies already exist, this paper argues for an automated signal processing at an expert level. The objective is to monitor a system over days, weeks, or years with as great accuracy as a human expert, and even better in regard to data investigation and analysis efficiency. After a data validation step most often ignored, any multimodal signal (vibration, current, acoustic, …) is processed over its entire frequency band in view of identifying all harmonic families and their sidebands. Sophisticated processing such as filtering and demodulation creates relevant features describing the fine complex structures of each spectrum. A time–frequency feature tracking constructs trends over time to not only detect a failure but also to characterize and localize it. Such an automated expert-level processing is a way to raise alarms with a reduced false alarm probability.

This study aims to examine the influence of colour exposure on noise annoyance. Previous studies in the literature have focused mostly on the effects of colour exposure on loudness judgements; however, due to the cognitive nature of multisensory perception, the influence of colour on noise annoyance also needs to be investigated. Our experiments were designed to administer non-information-carrying sound signals (i.e. white noise) and visual stimuli (i.e. abstract colour samples) and to limit visual and auditory contextual information. Participants were asked to evaluate noise annoyance on an 11-point International Commission on Biological Effects of Noise (ICBEN) scale. The experiments were conducted in the form of audio-visual tests. During these tests, random combinations of three white noise sound samples with sound pressure levels of 66 dB(A) (−4 dB[A] acoustic condition), 70 dB(A) (0 dB[A] acoustic condition) and 74 dB(A) (+4 dB[A] acoustic condition), and six visual stimuli, including the elementary colours of the Natural Colour System (NCS)—yellow (Y), red (R), blue (B), green (G), white (W) and black (S)—were presented to a total of 42 participants. The black colour sample was used to measure the audio-only control condition for the three white noise sound samples. The results of the study reveal that the effects of sound, the effects of colour and the interaction effects of colour and sound on perceived noise annoyance were statistically significant. The effects of colour on the loudness evaluations of the previous studies and the effects of colour on noise annoyance evaluations presented in this study show very similar and concordant results, indicating that the effects of colour on noise annoyance depend on the sound pressure level (SPL). The results indicate that the hue contrasts of red–green, red–blue and yellow–blue and the lightness contrast of yellow–blue influenced perceived noise annoyance when the SPL was low or high. Within the contrast pairs, red and yellow were perceived to be annoying, whereas blue and green were perceived to be non-annoying.

Fitness culture has significantly grown since the nineteenth century. In the recent decade, the gym and fitness industry have thrived in many countries. The construction of gymnasiums has increased dramatically, and fitness centres have become one of the most common spaces in mix-used buildings around the world. There are a significant number of gyms located relatively close to residential areas, some of which have even proposed to operate 24 h a day. The noise and vibration generated by dropping free weights in the gym affect the user experience and the surrounding community to a certain extent. In addition, today’s gyms are frequently operated out of refurbished retail units. Most refurbished unit buildings’ structures were never designed to host a gym, which makes the mitigation of noise and vibration very difficult. Based on critical literature reviews, the use of gymnasiums flooring system is relatively straightforward to mitigate the noise and vibration, but its effectiveness is hardly monitored. This study mainly discusses the use of material for mitigating noise and vibration in the gymnasiums together with the crowd sensing evaluation of vibration effectiveness, uncertainty and material deterioration to manage appropriate level of noise and vibration. The gym at the University of Birmingham has been chosen as a case study. Over 10 h of field tests have been conducted to record data of the operating floor material in the gym and fitness centre using novel smartphone sensors. Considering the specific floor material, a strategy of reducing noise and vibration is proposed. In addition, health and safety assessments are also carried out to evaluate the public safety condition in the gym. The insight into novel crowdsourcing smartphone sensors can help end-users to monitor the environmental impacts around the gyms and surroundings in real time.

A statistical energy analysis (SEA) model of a 510 l capacity refrigerator is used to calculate the sound pressure level generated in a reverberation room. This is a reliable indicator of the vibroacoustic performance of the refrigerator, and it is in some countries as a pre-commercialization test. The main contributions of the SEA model are the characterization of the refrigerator structure (three-layer: HIPS, polyurethane foam and steel) and the modelling of important components such as internal chambers or ventilation gratings. The simulation results are successfully compared with laboratory measurements. The SEA model is then used to understand the vibroacoustic behaviour of the refrigerator and to establish the most critical transmission paths and radiation mechanisms.

The source parameters of Irrawaddy dolphins’ echolocation click in the Bay of Brunei were estimated. Analysis of eight parameters shows that the Irrawaddy dolphins produce broadband echolocation clicks with mean click duration of 21.1 ± 7.2 μs. The clicks had a mean peak-to-peak apparent source level (ASLpp) of 201.2 ± 7.3 dB re 1 μPa (N = 350), a mean peak frequency of 116.5 ± 15.2 kHz, a mean centroid frequency of 115.4 ± 13.9 kHz, − 3 dB bandwidth of 51.8 ± 17.7 kHz, and − 10 dB bandwidth of 100.9 ± 20.3 kHz. Foraging behaviour was characterized by the high movement of the animals in various directions with no obvious pattern and frequent deep dives. The source parameters of the Brunei Bay Irrawaddy dolphins’ clicks from the present study were compared to those of the populations in Bangladesh and Thailand. The apparent source level and frequency range of clicks for the population in Brunei Bay were wider than those of the population in Bangladesh and Thailand. The variations in the measured parameters might be due to environmental factors or behaviour related.

The sound quality of a vehicle equipped with an inline four-cylinder engine is poor at around 2250 rpm with characteristic of prominent pure tone. The experimental results indicate that the second (2nd)-order intake noise has a peak at this engine speed. To understand the mechanism and solve this problem, the finite element model of the entire intake system is established to calculate the acoustic mode. Meanwhile, the 1D computational fluid dynamics (CFD) model integrating the intake/exhaust system with the engine is constructed to predict the intake noise. The modal analysis results demonstrate that the natural frequency of the intake system is close to the frequency of the 2nd-order periodic pressure pulsation noise at around 2250 rpm. The resonance of the air column is the main cause of the above-mentioned problem. The results of 1D-CFD simulation agree well with the experiment. Based on the analysis results and considering the constrains of engine cabin space, three kinds of control measures are proposed, which include changing the length of intake dirty pipe, changing the length of the clean pipe and installing the Helmholtz resonator. The simulation results are as follows: (1) By increasing the length of intake dirty pipe appropriately, the 2nd-order noise is reduced at low engine speed, while increased at high engine speed. (2) The length of the intake clean pipe has obvious influence on the acoustic mode of the intake system, but little influence on the total noise, and the 2nd-order noise attenuation is not obvious. (3) Helmholtz resonator can attenuate the total noise and the 2nd-order noise in most of engine speed range. Therefore, the Helmholtz resonator is used for the solution. The acoustical characteristics of the intake noise and the vehicle sound quality are improved.

Currently, the design of the urban soundscapes has been approached from distinct perspectives, which include, in all cases, the evaluation of how people perceive sound. An effective way to know how people experience an acoustic environment is through the use of simulators, where the inhabitants can design and establish their preferred sound levels. Making these tools available to different urban specialists, so they can be applied in individual common working environments, will allow them to be incorporated into the distinct stages of urban design, considerably improving solutions that promote more pleasant and restorative soundscapes for users. This paper describes the design and testing of a software based on a portable simulation tool whose function is to evaluate the acceptability of a variety of environmental noise sources categorized in four groups—water, birds, music and people. The experiment also evaluates the acceptability of these sounds sources with and without traffic noise mixed into the replayed sound. In order to validate it, two experiments with separate groups of people have been applied in two different places. With the exception of the “people” group of sounds, the results showed no correlation between sound level and acceptability, vibrancy, calmness both with and without the traffic and also the same preferred sounds were chosen in both places. These results suggest that it is possible to make prior participatory listening tests out of the laboratory. Finally, the participants commented that this experience improves their understanding of the acoustic environment and makes them aware of it.