Wireless Acoustic Sensor Networks Research Articles

Speech Intelligibility Analysis and Approximation to Room Parameters through the Internet of Things

In recent years, Wireless Acoustic Sensor Networks (WASN) have been widely applied to different acoustic fields in outdoor and indoor environments. Most of these applications are oriented to locate or identify sources and measure specific features of the environment involved. In this paper, we study the application of a WASN for room acoustic measurements. To evaluate the acoustic characteristics, a set of Raspberry Pi 3 (RPi) has been used. One is used to play different acoustic signals and four are used to record at different points in the room simultaneously. The signals are sent wirelessly to a computer connected to a server, where using MATLAB we calculate both the impulse response (IR), and different acoustic parameters, such as the Speech Intelligibility Index (SII). In this way, the evaluation of room acoustic parameters with asynchronous IR measurements two different applications has been explored. Finally, the network features have been evaluated to assess the effectiveness of this system.

Autonomous underwater vehicles support for enhanced performance in the Internet of underwater things

AbstractThe Internet of underwater things (IoUT) has become a promising and vibrant paradigm which mainly relies on the underwater acoustic wireless sensor networks (UASN). IoUT was first proposed to facilitate offshore real‐time monitoring and exploration of underwater environments. It is now becoming a key component for building and connecting the futuristic underwater smart cities. However, Acoustic waves used in underwater communication suffer from long propagation delays and high transmission energy requirements. Therefore, approaches for autonomous underwater vehicle (AUV) assisted data collection and cluster‐based routing were proposed to overcome these drawbacks. In this article, we present a smart genetic‐based AUV path planning algorithm for data collection to enhance the performance of UASN. In addition, we combine our smart data collection technique with a dynamic location‐unaware clustering algorithm to further reduce energy consumption with mobility consideration. Our simulation results showed that our genetic‐based technique achieves more than 145% increase in network lifetime and almost a 75% reduction in energy expenditure compared with traditional diagonal path selection technique. More simulations comparing our combined technique with a novel clustering technique showed that the network lifetime is twice as better, total energy consumption is nearly 30% less, and the delivery ratio is 15% more at low densities.

Double-Cross-Correlation Processing for Blind Sampling-Rate and Time-Offset Estimation

Coherent processing of signals captured by a wireless acoustic sensor network (WASN) requires an estimation of unknown parameters such as the sampling-rate and sampling-time offset (SRO and STO) of asynchronous sensor clocks. Although some sophisticated techniques for blind parameter estimation have become available in this young field of research, further development of new methods is required especially regarding environmental robustness, online estimation, and computational efficiency. As the main contribution of this work, we therefore introduce a novel approach for blind SRO estimation in the spirit of both a recently available time-domain double-cross-correlation processor (DXCP) and the well-known generalized cross-correlation with phase transform (GCC-PhaT). For the proposed approach, called DXCP-PhaT, we specifically introduce secondary cross-quantities to restore ergodicity of the otherwise drifting cross-correlation function or cross-spectrum of asynchronous input signals, which is an important property for estimation. Based upon this theoretical contribution of the paper, we then derive the DXCP-PhaT algorithm in the STFT domain, which brings along a number of improvements over state-of-the-art: advanced accuracy in terms of SRO assessment, environmental robustness to larger microphone distances as of real sensor networks, realtime-applicability in terms of online architecture and reduced complexity, and eventually an extension with online STO estimation (up to the inherent ambiguity of digital STO and acoustic TDOA) reliant on SRO compensation. Those claims are confirmed by comprehensive experiments, both offline and online, on simulated data and real recordings from a Rasberry-Pi-based WASN.

Sensor Selection for Relative Acoustic Transfer Function Steered Linearly-Constrained Beamformers

For multi-microphone speech enhancement, different microphones might have different contributions, assome are even marginal. This is more likely to happen in wireless acoustic sensor networks (WASNs), where somesensors might be distant. In this work, we therefore consider sensor selection for linearly-constrained beamformers. Theproposed sensor selection approach is formulated by minimizing the total output noise power and constraining thenumber of selected sensors. As the considered sensor selection problem requires the relative acoustic transfer function(RTF), the covariance whitening based RTF estimation or a direct-path RTF approximation is exploited. For a singletarget source, we can thus substitute the estimated RTF or the assumed RTF to the original problem formulation in orderto design a minimum variance distortionless response (MVDR) beamformer. Alternatively, we can integrate the two RTFsto design a linearly constrained minimum variance (LCMV) beamformer in order to alleviate the effects of RTFestimation/approximation errors. By leveraging the superiority of LCMV beamformers, the proposed approach can beapplied to the multi-source case. An evaluation using a simulated large-scale WASN demonstrates that the integration ofRTFs for the sensor selection based LCMV beamformer can be beneficial as opposed to relying on either of theindividual RTF steered sensor selection based MVDR beamformers. We conclude that the sensors that are close to thetarget source(s) and also some around the coherent interferers are more informative.

Distributed Adaptive Signal Estimation in Wireless Sensor Networks With Partial Prior Knowledge of the Desired Sources Steering Matrix

In the past two decades, wireless sensor networks (WSNs) and their applications have been the topic of many studies. Different multi-sensor nodes are used to collect, process and distribute data over wireless links to perform different tasks such as smart detection, target tracking, node localization, etc. In this article, the problem of distributed adaptive estimation of node-specific signals for signal enhancement or noise reduction is addressed. First the centralized rank R generalized eigenvalue decomposition (GEVD) based multichannel Wiener filter (MWF) with prior knowledge for node-specific signal estimation in a WSN is introduced, where (some of) the nodes have partial prior knowledge of the desired sources steering matrix. A distributed adaptive estimation algorithm for a fully-connected WSN is then proposed demonstrating that this MWF can be obtained by letting the nodes work on compressed (i.e. reduced-dimensional) sensor signals compared to the centralized algorithm. The distributed algorithm can be used in applications such as speech enhancement in a wireless acoustic sensor network (WASN), where (some of) the nodes have prior knowledge on the location of the desired speech sources and on their local microphone array geometry or have access to clean noise reference signals. Foundations for a proof of convergence using a Lagrangian framework, are given, since convergence is observed in batch-mode simulations. Finally, numerical simulation results are provided for a speech enhancement scenario.

Enhancement by postfiltering for speech and audio coding in ad hoc sensor networks

Enhancement algorithms for wireless acoustic sensor networks (WASNs) are indispensable with the increasing availability and usage of connected devices with microphones. Conventional spatial filtering approaches for enhancement in WASNs approximate quantization noise with an additive Gaussian distribution, which limits performance due to the non-linear nature of quantization noise at lower bitrates. This work proposes a postfilter for enhancement based on Bayesian statistics to obtain a multidevice signal estimate, which explicitly models the quantization noise. The experiments using perceptual signal-to-noise ratio, perceptual evaluation of speech quality, and MUSHRA (multistimulus with hidden reference and anchors) scores demonstrate that the proposed postfilter can be used to enhance signal quality in ad hoc sensor networks.

Design of Algorithms and Protocols for Underwater Acoustic Wireless Sensor Networks

Nowadays, with the recent advances of wireless underwater communication and acoustic sensor devices technology, we are witnessing a surge in the exploration and exploitation of the ocean’s abundant natural resources. Accordingly, to fulfill the requirements of the exploration of the ocean, researchers have focused their work toward the design of methods and algorithms for the underwater acoustic sensor networks (UASNs). Although considerable research effort has been devoted to the development of a variety of UASN-based applications, very limited work has addressed the algorithmic design and analysis for UASN. To this end, we propose to provide a comprehensive design, development, and analysis of algorithms and protocols for UASNs. We discuss each of the fundamental UASN building blocks, such as (i) underwater acoustic communication channel modeling, (ii) sustainable coverage and target detection, (iii) Medium Access Control (MAC-layer design and time synchronization, (iv) localization algorithms design, and (v) underwater routing protocol. Then, we illustrate the different protocols from each category and compare their benefits and drawbacks. Finally, we discuss a few potential directions for future research related to the design of future generations of UASNs.

Lightweight feature extraction method for efficient acoustic-based animal recognition in wireless acoustic sensor networks

Wireless acoustic sensor networks represent an attractive solution that can be deployed for animal detection and recognition in a monitored area. A typical configuration for this application would be to transmit the whole acquired audio signal through multi-hop communication to a remote server for recognition. However, continuous data streaming can cause a severe decline in the energy of the sensors, which consequently reduces the network lifetime and questions the viability of the application. An efficient solution to reduce the sensor's radio activity would be to perform the recognition task at the source sensor then to communicate the result to the remote server. This approach is intended to save the energy of the acoustic source sensor and to unload the network from carrying, probably, useless data. However, the validity of this solution depends on the energy efficiency of performing on-sensor detection of a new acoustic event and accurate recognition. In this context, this paper proposes a new scheme for on-sensor energy-efficient acoustic animal recognition based on low-complexity methods for feature extraction using the Haar wavelet transform. This scheme achieves more than 86% in recognition accuracy while saving 71.59% of the sensor energy compared with the transmission of the raw signal.

BCNDataset: Description and Analysis of an Annotated Night Urban Leisure Sound Dataset

Acoustic pollution has been associated with adverse effects on the health and life expectancy of people, especially when noise exposure happens during the nighttime. With over half of the world population living in urban areas, acoustic pollution is an important concern for city administrators, especially those focused on transportation and leisure noise. Advances in sensor and network technologies made the deployment of Wireless Acoustic Sensor Networks (WASN) possible in cities, which, combined with artificial intelligence (AI), can enable smart services for their citizens. However, the creation of such services often requires structured environmental audio databases to train AI algorithms. This paper reports on an environmental audio dataset of 363 min and 53 s created in a lively area of the Barcelona city center, which targeted traffic and leisure events. This dataset, which is free and publicly available, can provide researchers with real-world acoustic data to help the development and testing of sound monitoring solutions for urban environments.

A Feasibility Analysis of an Application-Based Partial Initialization (API) Protocol for Underwater Wireless Acoustic Sensor Networks.

Initialization methods for underwater wireless acoustic sensor networks (UWASNs) have been proposed as a subset of other network protocols under the simple assumption that all the nodes in the network can be initialized at once. However, it is generally time- and energy-intensive to initialize all nodes in a UWASN due to unstable underwater channel conditions. To improve network efficiency, we propose the Application-based Partial Initialization (API) protocol, which initializes only the same number of nodes as the number of activated nodes required to run a specific application. Reducing the number of active nodes is also particularly advantageous underwater since the replacement of batteries is costly. To the best of our knowledge, the API is the first approach that initializes nodes partially according to applications. Thus, we investigate the feasibility of the API for a UWASN by analyzing its performance via simulations. From the results, it is shown that the API provides similar data statistics compared with the conventional full initialization that initializes all nodes. Moreover, the API outperforms the full initialization in terms of the initialization time and message overhead performances.

Enabling Real-Time Computation of Psycho-Acoustic Parameters in Acoustic Sensors Using Convolutional Neural Networks

Sensor networks have become an extremely useful tool for monitoring and analysing many aspects of our daily lives. Noise pollution levels are very important today, especially in cities where the number of inhabitants and disturbing sounds are constantly increasing. Psycho-acoustic parameters are a fundamental tool for assessing the degree of discomfort produced by different sounds and, combined with wireless acoustic sensor networks (WASNs), could enable, for example, the efficient implementation of acoustic discomfort maps within smart cities. However, the continuous monitoring of psycho-acoustic parameters to create time-dependent discomfort maps requires a high computational demand that prevents real-time computations within the nodes. Moreover, sending audio streams outside of the WASN for their further computation, would require extra communication and computational efforts without warranting a real-time monitoring, with the added problem of violating some privacy laws. As a result, most existing systems for nuisance assessment are usually based on less accurate indicators that require lower computational cost. In this paper, we describe the design and analysis of a deep convolutional neural network (CNN) trained with a big dataset of typical sounds occurrying in a city. The CNN allows to predict the psycho-acoustic parameters considered by the well-known Zwicker’s psycho-acoustic nuisance model with great accuracy, directly from the raw recorded audio signal. The proposed CNN-based system has been tested on both desktop computers and typical WASN devices (such as Raspberry Pi), achieving very fast calculation times that allow real-time operation and a continuous monitoring of psycho-acoustic parameters.

Multi-Agent-Based Acoustic Sensor Node Deployment in Underwater Acoustic Wireless Sensor Networks

The deployment of acoustic sensor nodes in 3-D underwater acoustic wireless sensor networks (UAWSN) is a difficult task due to various aquatic conditions and physical obstacles. This work proposes multi-agent-based acoustic sensor node deployment (MASD) to deploy the acoustic nodes at ideal positions to enhance coverage and seamless connectivity. The proposed scheme works is threefold: 1) AUV initiates random walk in the network to gather the information and prospective common reference points; 2) the base station gets this information through surface buoys and computes the routing path, feasible locations for deploying new nodes; and 3) AUV collects this information and follows the path to deploy nodes with the help of agents. The multi-agent-enabled deployment framework (MADF) is proposed to support the deployment process at each level of the proposed MASD scheme. The performance of propagation loss, coverage, and overhead tradeoffs are analyzed to validate the proposed scheme. Mobility issues can be further re-investigated in shallow water as a future direction to the MASD scheme.

Long-Term Temporal Analysis of Psychoacoustic Parameters of the Acoustic Environment in a University Campus Using a Wireless Acoustic Sensor Network

Higher education institution campuses have been growing in size to become small cities that encompass all the infrastructures and offer many services to the university community. In order to achieve sustainable growth, it is necessary to take into account different environmental aspects, including the acoustic environment. In this paper, the application of a wireless acoustic sensor network is proposed for the long-term analysis of psychoacoustic parameters of the acoustic environment in a university campus. A network of acoustic nodes is designed and four nodes are deployed at both indoor and outdoor locations of the campus for environmental noise monitoring. A measurement campaign has been running continuously for nine months, including three months in which the state of emergency due to COVID-19 pandemic was established and the university switched to online classes. Acquired and stored data include not only the basic sound level parameters, e.g., equivalent or percentile sound pressure levels, but also psychoacoustic parameters, that are more related with noise assessment of the environment, e.g., loudness or sharpness sound levels. After analysing the acoustic environment of the campus through the complete set of nodes, results show two zones with higher noise indicators where some action plan should be developed. A detailed analysis of the temporal evolution of noise levels in these acoustically saturated zones is done, comparing values between a period of regular activity and the period of state of emergency. Different frequency distributions of sound levels are detected between day and evening periods providing a better insight into the noise sources. The results also show that binaural psychoacoustic annoyance values are correlated with loudness values in these locations, however sharpness values have minor effects.

The Internet of Audio Things: State of the Art, Vision, and Challenges

The Internet of Audio Things (IoAuT) is an emerging research field positioned at the intersection of the Internet of Things, sound and music computing, artificial intelligence, and human-computer interaction. The IoAuT refers to the networks of computing devices embedded in physical objects (Audio Things) dedicated to the production, reception, analysis, and understanding of audio in distributed environments. Audio Things, such as nodes of wireless acoustic sensor networks, are connected by an infrastructure that enables multidirectional communication, both locally and remotely. In this article, we first review the state of the art of this field, then we present a vision for the IoAuT and its motivations. In the proposed vision, the IoAuT enables the connection of digital and physical domains by means of appropriate information and communication technologies, fostering novel applications and services based on auditory information. The ecosystems associated with the IoAuT include interoperable devices and services that connect humans and machines to support human-human and human-machines interactions. We discuss the challenges and implications of this field, which lead to future research directions on the topics of privacy, security, design of Audio Things, and methods for the analysis and representation of audio-related information.

WASN-Based Day–Night Characterization of Urban Anomalous Noise Events in Narrow and Wide Streets

In addition to air pollution, environmental noise has become one of the major hazards for citizens, being Road Traffic Noise (RTN) as its main source in urban areas. Recently, low-cost Wireless Acoustic Sensor Networks (WASNs) have become an alternative to traditional strategic noise mapping in cities. In order to monitor RTN solely, WASN-based approaches should automatize the off-line removal of those events unrelated to regular road traffic (e.g., sirens, airplanes, trams, etc.). Within the LIFE DYNAMAP project, 15 urban Anomalous Noise Events (ANEs) were described through an expert-based recording campaign. However, that work only focused on the overall analysis of the events gathered during non-sequential diurnal periods. As a step forward to characterize the temporal and local particularities of urban ANEs in real acoustic environments, this work analyses their distribution between day (06:00–22:00) and night (22:00–06:00) in narrow (1 lane) and wide (more than 1 lane) streets. The study is developed on a manually-labelled 151-h acoustic database obtained from the 24-nodes WASN deployed across DYNAMAP’s Milan pilot area during a weekday and a weekend day. Results confirm the unbalanced nature of the problem (RTN represents 83.5% of the data), while identifying 26 ANE subcategories mainly derived from pedestrians, animals, transports and industry. Their presence depends more significantly on the time period than on the street type, as most events have been observed in the day-time during the weekday, despite being especially present in narrow streets. Moreover, although ANEs show quite similar median durations regardless of time and location in general terms, they usually present higher median signal-to-noise ratios at night, mainly on the weekend, which becomes especially relevant for the WASN-based computation of equivalent RTN levels.

Design of a Low-Cost Configurable Acoustic Sensor for the Rapid Development of Sound Recognition Applications

Concerned about the noise pollution in urban environments, the European Commission (EC) has created an Environmental Noise Directive 2002/49/EC (END) requiring Member states to publish noise maps and noise management plans every five years for cities with a high density of inhabitants, major roads, railways and airports. The END also requires the noise pressure levels for these sources to be presented independently. Currently, data measurements and the representations of the noise pressure levels in such maps are performed semi-manually by experts. This process is time and cost consuming, as well as limited to presenting only a static picture of the noise levels. To overcome these issues, we propose the deployment of Wireless Acoustic Sensor Networks with several nodes in urban environments that can enable the generation of real-time noise level maps, as well as detect the source of the sound thanks to machine learning algorithms. In this paper, we briefly review the state of the art of the hardware used in wireless acoustic applications and propose a low-cost sensor based on an ARM cortex-A microprocessor. This node is able to process machine learning algorithms for sound source detection in-situ, allowing the deployment of highly scalable sound identification systems.

Distributed Speech Presence Probability Estimator in Fully Connected Wireless Acoustic Sensor Networks

This paper presents a Gaussian-based distributed speech presence probability (DSPP) estimator which is applied in fully connected wireless acoustic sensor networks (WASNs). In WASNs, we are primarily interested in optimally utilizing all available information of recorded signals. In this work, under the Gaussian statistical assumption of signals, each node computes the DSPP using its own local signals along with the compressed signals from other nodes. We evaluate the effect of DSPP estimation on noise reduction from both the simulated and the real recorded signals. The performance of the proposed DSPP estimator is compared to that of local SPP estimation, where each node only uses its noisy signals, and to that of centralized SPP estimation, where each node uses all recorded noisy signals of the whole network. It is shown that the proposed method exhibits good performance, while the computational complexity is considerably reduced.

Model-based distributed node clustering and multi-speaker speech presence probability estimation in wireless acoustic sensor networks.

The knowledge of speech presence probability (SPP) plays an essential role in noise estimation and speech enhancement. Single channel SPP estimation and centralized multi-channel SPP estimation have been well studied. However, how to estimate SPP in wireless acoustic sensor networks (WASNs) remains a great challenge and few efforts can be found in this topic, particularly for WASN applications with multiple speakers. Accordingly, this paper is devoted to the problem of SPP estimation in WASNs and it presents a distributed model-based SPP estimation method for multi-speaker detection, which does not need any fusion center. A distributed k-means clustering method is first used to cluster the nodes into subnetworks, which detect different speakers. For each node in the subnetwork, the speech and noise power spectral densities are estimated locally by using a model-based method, then a distributed SPP estimator is developed and applied in every subnetwork. A distributed consensus method is used to obtain the distributed clustering and the distributed SPP estimation. Simulation results show that the proposed distributed clustering method can assign nodes into subnetworks based on their noisy observations. Moreover, the proposed distributed SPP estimator achieves robust speech detection performance under different noise conditions.

Joint Sampling Synchronization and Source Localization for Wireless Acoustic Sensor Networks

In wireless acoustic sensor networks (WASNs), usually each sensor node has an individual clock source and captures the acoustic sources asynchronously. Due to the independent oscillators, a sampling rate offset (SRO) is inevitable between different devices. The SRO has an adverse effect on the performance of algorithms. In this work, we propose a method for estimating the SROs and time difference of arrivals (TDOAs) jointly. The SROs and TDOAs are firstly approximated by a linear-phase difference model in the short-time Fourier transform domain, and then estimated by a least-squares estimator. Further, we analyze the Cramer-Rao lower bound (CRLB) for the considered estimator. Simulation results show that for both SRO estimation and source localization problems, the proposed method achieves an improvement for asynchronous WASNs.

Wireless Sensor Networks for Noise Measurement and Acoustic Event Recognitions in Urban Environments.

Nowadays, urban noise emerges as a distinct threat to people’s physiological and psychological health. Previous works mainly focus on the measurement and mapping of the noise by using Wireless Acoustic Sensor Networks (WASNs) and further propose some methods that can effectively reduce the noise pollution in urban environments. In addition, the research on the combination of environmental noise measurement and acoustic events recognition are rapidly progressing. In a real-life application, there still exists the challenges on the hardware design with enough computational capacity, the reduction of data amount with a reasonable method, the acoustic recognition with CNNs, and the deployment for the long-term outdoor monitoring. In this paper, we develop a novel system that utilizes the WASNs to monitor the urban noise and recognize acoustic events with a high performance. Specifically, the proposed system mainly includes the following three stages: (1) We used multiple sensor nodes that are equipped with various hardware devices and performed with assorted signal processing methods to capture noise levels and audio data; (2) the Convolutional Neural Networks (CNNs) take such captured data as inputs and classify them into different labels such as car horn, shout, crash, explosion; (3) we design a monitoring platform to visualize noise maps, acoustic event information, and noise statistics. Most importantly, we consider how to design effective sensor nodes in terms of cost, data transmission, and outdoor deployment. Experimental results demonstrate that the proposed system can measure the urban noise and recognize acoustic events with a high performance in real-life scenarios.