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Wireless sensor networks (WSN), as a solution for buried water pipe monitoring, face a new set of challenges compared to traditional application for above-ground infrastructure monitoring. One of the main challenges for underground WSN deployment is the limited range (less than 3 m) at which reliable wireless underground communication can be achieved using radio signal propagation through the soil. To overcome this challenge, the purpose of this paper is to investigate a new approach for wireless underground communication using acoustic signal propagation along a buried water pipe.

An acoustic communication system was developed based on the requirements of low cost (tens of pounds at most), low power supply capacity (in the order of 1 W-h) and miniature (centimetre scale) size for a wireless communication node. The developed system was further tested along a buried steel pipe in poorly graded SAND and a buried medium density polyethylene (MDPE) pipe in well graded SAND.

With predicted acoustic attenuation of 1.3 dB/m and 2.1 dB/m along the buried steel and MDPE pipes, respectively, reliable acoustic communication is possible up to 17 m for the buried steel pipe and 11 m for the buried MDPE pipe.

Although an important first step, more research is needed to validate the acoustic communication system along a wider water distribution pipe network.

This paper shows the possibility of achieving reliable wireless underground communication along a buried water pipe (especially non-metallic material ones) using low-frequency acoustic propagation along the pipe wall.

Ciliated microelectromechanical system (MEMS) vector hydrophones pick up sound signals through Wheatstone bridge in cross beam-ciliated microstructures to achieve information transmission. This paper aims to overcome the complexity and variability of the marine environment and achieve accurate location of targets. In this paper, a new method for ocean noise denoising based on improved complete ensemble empirical mode decomposition with adaptive noise combined with wavelet threshold processing method (CEEMDAN-WT) is proposed.

Based on the CEEMDAN-WT method, the signal is decomposed into different intrinsic mode functions (IMFs), and relevant parameters are selected to obtain IMF denoised signals through WT method for the noisy mode components with low sample entropy. The final pure signal is obtained by reconstructing the unprocessed mode components and the denoising component, effectively separating the signal from the wave interference.

The three methods of empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD) and CEEMDAN are compared and analyzed by simulation. The simulation results show that the CEEMDAN method has higher signal-to-noise ratio and smaller reconstruction error than EMD and EEMD. The feasibility and practicability of the combined denoising method are verified by indoor and outdoor experiments, and the underwater acoustic experiment data after processing are combined beams. The problem of blurry left and right sides is solved, and the high precision orientation of the target is realized.

This algorithm provides a theoretical basis for MEMS hydrophones to achieve accurate target positioning in the ocean, and can be applied to the hardware design of sonobuoys, which is widely used in various underwater acoustic work.

Dysarthria is a neuromotor speech disorder caused by neuromuscular disturbances that affect one or more articulators resulting in unintelligible speech. Though inter-phoneme articulatory variations are well captured by formant frequency-based acoustic features, these variations are expected to be much higher for dysarthric speakers than normal. These substantial variations can be well captured by placing sensors in appropriate articulatory position. This study focuses to determine a set of articulatory sensors and parameters in order to assess articulatory dysfunctions in dysarthric speech.

The current work aims to determine significant sensors and parameters associated using motion path and correlation analyzes on the TORGO database of dysarthric speech. Among eight informative sensor channels and six parameters per channel in positional data, the sensors such as tongue middle, back and tip, lower and upper lips and parameters (y, z, φ) are found to contribute significantly toward capturing the articulatory information. Acoustic and positional data analyzes are performed to validate these identified significant sensors. Furthermore, a convolutional neural network-based classifier is developed for both phone-and word-level classification of dysarthric speech using acoustic and positional data.

The average phone error rate is observed to be lower, up to 15.54% for positional data when compared with acoustic-only data. Further, word-level classification using a combination of both acoustic and positional information is performed to study that the positional data acquired using significant sensors will boost the performance of classification even for severe dysarthric speakers.

The proposed work shows that the significant sensors and parameters can be used to assess dysfunctions in dysarthric speech effectively. The articulatory sensor data helps in better assessment than the acoustic data even for severe dysarthric speakers.

The purpose of this study is to determine how sonic logo’s acoustic features (intensity, pitch and pace) based on melodic tunes with no voice orient the response of consumers, attract attention, elicit levels of pleasantness and calmness and transmit brand personality traits.

A within-subject experimental factorial design is applied to measure emotional arousal (indexed as electrodermal activity) and enhancement on perceptual processing (indexed as heart rate), as well as self-reported factors, namely, calmness/excitement, pleasantness and brand personality scales.

Results show a significant increase on electrodermal activity associated with fast-paced sonic logos and a decrease in heart rate in slow-paced long sonic logos. Also, fade-up, pitch-ascending fast sonic logos are defined as more exciting and descending-pitch sonic logos as more pleasant.

The use of sonic logos with no voice does limit its implications. Besides, the use of three variables simultaneously with 18 versions of sonic logos in a laboratory setting may have driven participants to fatigue; hence, findings should be cautiously applied.

First, sonic logos are best processed in a fade-up form. Second, fast pace is recommended to orient response, whereas slow pace is recommended to transmit calmness. Practitioners may opt for fast-paced sonic logos if the design is new or played in a noisy environment and opt for slow-paced sonic logos in already highly recognized sound designs.

To the best of authors’ knowledge, this study is the first to combine psychophysiological measures and self-reported scales in a laboratory experiment on how sonic logo’s acoustic features orient response, transmit emotions and personality traits.

Using a novel measure incorporating stylistic and acoustic data on recorded music from 1967 to 2017, we search for trends in the evolution of musical diversity in 125,340 albums. We find that temporal patterns of diversity differ for stylistic and acoustic data. We also find that the patterns differ dramatically by genre. Some genres, such as blues, jazz, and pop-rock, decrease in diversity over time; most other genres increase in diversity. The causes of these different trends present a puzzle for future research. We also find different patterns for recordings that made the Billboard 200 charts compared to all recordings, suggesting an association between selection processes driven by consumer popularity and diversity. Moreover, associations of diversity and industry structure found in prior research do not hold when we analyze data beyond the smaller sample of the more popular recordings found in Billboard. These findings have implications for many prior studies based exclusively on best-selling recordings

The Reflective Middleware for Acoustic Management (ReM-AM), based on the Middleware for Cloud Learning Environments (AmICL), aims to improve the interaction between users and agents in a Smart Environment (SE) using acoustic services, in order to consider the unpredictable situations due to the sounds and vibrations. The middleware allows observing, analyzing, modifying and interacting in every state of a SE from the acoustics. This work details an extension of the ReM-AM using the ontology-driven architecture (ODA) paradigm for acoustic management.

This work details an extension of the ReM-AM using the ontology-driven architecture (ODA) paradigm for acoustic management. In this paper are defined the different domains of knowledge required for the management of the sounds in SEs, which are modeled using ontologies.

This work proposes an acoustics and sound ontology, a service-oriented architecture (SOA) ontology, and a data analytics and autonomic computing ontology, which work together. Finally, the paper presents three case studies in the context of smart workplace (SWP), ambient-assisted living (AAL) and Smart Cities (SC).

Future works will be based on the development of algorithms for classification and analysis of sound events, to help with emotion recognition not only from speech but also from random and separate sound events. Also, other works will be about the definition of the implementation requirements, and the definition of the real context modeling requirements to develop a real prototype.

In the case studies is possible to observe the flexibility that the ReM-AM middleware based on the ODA paradigm has by being aware of different contexts and acquire information of each, using this information to adapt itself to the environment and improve it using the autonomic cycles. To achieve this, the middleware integrates the classes and relations in its ontologies naturally in the autonomic cycles.

The main contribution of this work is the description of the ontologies required for future works about acoustic management in SE, considering that what has been studied by other works is the utilization of ontologies for sound event recognition but not have been expanded like knowledge source in an SE middleware. Specifically, this paper presents the theoretical framework of this work composed of the AmICL middleware, ReM-AM middleware and the ODA paradigm.

This research deals with the production of electronic textiles (e‐textiles) demonstrators. Initially, the research dealt with the creation of 4×5 microphone array on a large area conformal textile substrate. Once the interface electronics were connected to the 4×5 microphone array, this system became an effective acoustic array. Here, a new acoustic eight microphone array design has been designed, fabricated and tested. Changes were made to improve microphone array performance, and to optimize the associated software for data capture and analysis. This new design was based on UC‐Berkeley mote microcomputer technology. The mote‐based system addresses the issue of scaling acoustic arrays, to allow for distributing microphones over large‐areas, and to allow performance comparisons to be made with the original 4×5 microphone acoustic array.

We review literature linking patterns of vocal accommodation in the paraverbal range of the voice to small group structures of status and dominance. We provide a thorough overview of the current state of vocal accommodation research, tracing the development of the model from its early focus on patterns of mutual vocal adaptation, to the current focus on structural factors producing patterns of unequal accommodation between group members. We also highlight gaps in existing knowledge and opportunities to contribute to the development of vocal accommodation as an unobtrusive, nonconscious measure of small group hierarchies.

We trace the empirical development of vocal accommodation as a measure of status and power, and discuss connections between vocal accommodation and two prominent theoretical frameworks: communication accommodation theory (CAT) and expectation states theory. We also provide readers with a guide for collecting and analyzing vocal data and for calculating two related measures of vocal accommodation.

Across multiple studies, vocal accommodation significantly predicts observers’ perceptions regarding interactants engaged in debates and interviews. Studies have specifically linked vocal accommodation to perceptions of relative power or dominance, but have not shown a relationship between accommodation and perceptions of prestige.

Vocal accommodation measures have clear applications for measuring and modeling group dynamics. More work is needed to understand how accommodation functions in clearly-defined status situations, how the magnitude of status differences affects the degree of accommodation inequality, and how vocal accommodation is related to other correlates of social status, including openness to influence and contributions to group tasks.

As oral communication in higher education is vital, good classroom acoustics is needed to pass the verbal message to university students. Non-auditory factors such as academic language, a non-native educational context and a diversity of acoustic settings in different types of classrooms affect speech understanding and performance of students. The purpose of this study is to find out whether the acoustic properties of the higher educational teaching contexts meet the recommended reference levels.

Background noise levels and the Speech Transmission Index (STI) were assessed in 45 unoccupied university classrooms (15 lecture halls, 16 regular classrooms and 14 skills laboratories).

The findings of this study indicate that 41 classrooms surpassed the maximum reference level for background noise of 35 dB(A) and 17 exceeded the reference level of 40 dB(A). At five-meter distance facing the speaker, six classrooms indicated excellent speech intelligibility, while at more representative listening positions, none of the classrooms indicated excellent speech intelligibility. As the acoustic characteristics in a majority of the classrooms exceeded the available reference levels, speech intelligibility was likely to be insufficient.

This study seeks to assess the acoustics in academic classrooms against the available acoustic reference levels. Non-acoustic factors, such as academic language complexity and (non-)nativeness of the students and teaching staff, put higher cognitive demands upon listeners in higher education and need to be taken into account when using them in daily practice for regular students and students with language/hearing disabilities in particular.

The purpose of this paper is to employ the acoustic wave propagation method for leakage detection in pipes. The first objective is to use acoustic finite element analysis (AFEA) method to simulate acoustic wave propagation and acoustic wave reflectometry in an intact pipe and in pipes with leaks of various sizes. This is followed by the second objective which is to validate the effectiveness and the practicability of the acoustic wave method via experimental testing. The third objective involves the decomposition and de-noising of the measured acoustic waves using stationary wavelet transform (SWT). It is shown that this approach, which is used for the first time on leakage detection in pipes, can be used to identify, locate and estimate the size of a leakage defect in a pipe.

The research work was designed inline with best practices and acceptable standards. The research methodology focusses on five basic areas: literature review; experimental measurements; simulations; data analysis and writing-up of the study with clear-cut communication of the findings. The approach used was acoustic wave propagation-based method in conjunction with SWT for leakage detection in fluid-filled pipe.

First, the simulation of acoustic wave propagation and acoustic wave reflectometry in fluid-filled pipes with and without leakage have great potential in leakage detection in pipeline systems and can detect very small leaks of 1 mm diameter. Second, the measured noise-contaminated acoustic wave propagation in a fluid-filled pipe can be successfully de-noised using the SWT method in order to clearly identify and locate leakage as little as 5 mm diameter in a pipe. Third, AFEA of a fluid-filled pipe can be achieved with the simulation of only the fluid content of the pipe and without the inclusion of the pipe in the model. This eliminates contact interaction of the solid pipe walls and the fluid, and as a consequence reduces computational time and resources. Fourth, the relationship of the ratio of the leakage diameter to the ratio of the first and second secondary wave amplitudes caused by the leakage can be represented by a second-order polynomial function. Fifth, the identification of leakage in a pipe is intuitive from mere comparison of the acoustic waveforms of an intact pipe with that of a pipe with a leakage.

The research work is a novelty and was developed from the scratch. The AFEA of acoustic wave propagation and acoustic wave reflectometry in a static fluid-filled pipe, and the SWT method have been used for the first time to detect, locate and estimate the size of a leakage in a fluid-filled pipe.