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Acoustic Sensing and Monitoring in Urban and Natural Environments
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Acoustic Sensing and Monitoring in Urban and Natural Environments

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Environmental Sensing".

Deadline for manuscript submissions: closed (20 April 2024) | Viewed by 17895

Special Issue Editor

Dr. Hector Eduardo Roman

E-Mail Website
Guest Editor
Department of Physics, University of Milano-Bicocca, Piazza della Scienza 3, 20126 Milan, Italy
Interests: environmental noise monitoring networks; road traffic noise and mapping predictions; sensor networks and their optimization; cloud and fog computing applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Human society tends to concentrate in urban areas to facilitate and improve its many different activities. Historically, such large-scale aggregation processes have evolved without long-term planning, and have not been designed to obtain optimized results. As a result, people living in large urban zones must cope with the deleterious action of many agents, of which anthropogenic noise plays a central role. It is indeed only recently that all these issues have started to be considered and studied seriously, guiding policy-making decisions to envisage the right intervention measures.

It is well-known that noise pollution is an issue affecting millions of people worldwide, and is currently considered one of the greatest environmental threats to people’s health. During the last several decades, great advances in sensor technology and monitoring strategies have helped to quantify anthropogenic noise pollution in both urban and natural environments. Clearly, sound from natural habitats lacking human influence can be considered as a benchmark to estimate the impact of anthropogenic activity in a particular surrounding. This observation may ultimately lead to the development of new approaches aimed at improving the quality of life in the presence of noise pollution.

The aim of this Special Issue is to gather experts actively working in different fields of acoustic phenomena, such as sensing and monitoring techniques in either urban or natural environments, to share their research work in the form of original papers or reviews to expand our knowledge on noise pollution. The present Issue welcomes contributions within, but not limited to, the following topics:

  • Noise source identification and sound source location.
  • IoT-enabled acoustic sensing platforms.
  • Distributed acoustic sensing for urban subsurface/surface monitoring.
  • Wireless acoustic sensor networks.
  • Urban sound landscape and its corresponding noise identification.
  • Eco-acoustic indices time series.
  • Monitoring of different types of noise, including wheel-rail noise, environmental noise, airborne noise, architectural acoustics, and vehicle noise.
  • Noise in natural environments, including earth surface and marine/submarine locations.
  • Sensor location distribution.
  • New methods for acoustic data processing based on AI approaches.

Dr. Hector Eduardo Roman
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (14 papers)

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19 pages, 6386 KiB  
Article
Directional Multi-Resonant Micro-Electromechanical System Acoustic Sensor for Low Frequency Detection
by Justin Ivancic and Fabio Alves
Sensors 2024, 24(9), 2908; https://doi.org/10.3390/s24092908 - 2 May 2024
Viewed by 357
Abstract
This paper reports on the design, modeling, and characterization of a multi-resonant, directional, MEMS acoustic sensor. The design builds on previous resonant MEMS sensor designs to broaden the sensor’s usable bandwidth while maintaining a high signal-to-noise ratio (SNR). These improvements make the sensor [...] Read more.
This paper reports on the design, modeling, and characterization of a multi-resonant, directional, MEMS acoustic sensor. The design builds on previous resonant MEMS sensor designs to broaden the sensor’s usable bandwidth while maintaining a high signal-to-noise ratio (SNR). These improvements make the sensor more attractive for detecting and tracking sound sources with acoustic signatures that are broader than discrete tones. In-air sensor characterization was conducted in an anechoic chamber. The sensor was characterized underwater in a semi-anechoic pool and in a standing wave tube. The sensor demonstrated a cosine-like directionality, a maximum acoustic sensitivity of 47.6 V/Pa, and a maximum SNR of 88.6 dB, for 1 Pa sound pressure, over the bandwidth of the sensor circuitry (100 Hz–3 kHz). The presented design represents a significant improvement in sensor performance compared to similar resonant MEMS sensor designs. Increasing the sensitivity of a single-resonator design is typically associated with a decrease in bandwidth. This multi-resonant design overcomes that limitation. Full article
(This article belongs to the Special Issue Acoustic Sensing and Monitoring in Urban and Natural Environments)
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19 pages, 4051 KiB  
Article
Coupling Different Road Traffic Noise Models with a Multilinear Regressive Model: A Measurements-Independent Technique for Urban Road Traffic Noise Prediction
by Domenico Rossi, Antonio Pascale, Aurora Mascolo and Claudio Guarnaccia
Sensors 2024, 24(7), 2275; https://doi.org/10.3390/s24072275 - 3 Apr 2024
Viewed by 487
Abstract
Road traffic noise is a severe environmental hazard, to which a growing number of dwellers are exposed in urban areas. The possibility to accurately assess traffic noise levels in a given area is thus, nowadays, quite important and, on many occasions, compelled by [...] Read more.
Road traffic noise is a severe environmental hazard, to which a growing number of dwellers are exposed in urban areas. The possibility to accurately assess traffic noise levels in a given area is thus, nowadays, quite important and, on many occasions, compelled by law. Such a procedure can be performed by measurements or by applying predictive Road Traffic Noise Models (RTNMs). Although the first approach is generally preferred, on-field measurement cannot always be easily conducted. RTNMs, on the contrary, use input information (amount of passing vehicles, category, speed, among others), usually collected by sensors, to provide an estimation of noise levels in a specific area. Several RTNMs have been implemented by different national institutions, adapting them to the local traffic conditions. However, the employment of RTNMs proves challenging due to both the lack of input data and the inherent complexity of the models (often composed of a Noise Emission Model–NEM and a sound propagation model). Therefore, this work aims to propose a methodology that allows an easy application of RTNMs, despite the availability of measured data for calibration. Four different NEMs were coupled with a sound propagation model, allowing the computation of equivalent continuous sound pressure levels on a dataset (composed of traffic flows, speeds, and source–receiver distance) randomly generated. Then, a Multilinear Regressive technique was applied to obtain manageable formulas for the models’ application. The goodness of the procedure was evaluated on a set of long-term traffic and noise data collected in a French site through several sensors, such as sound level meters, car counters, and speed detectors. Results show that the estimations provided by formulas coming from the Multilinear Regressions are quite close to field measurements (MAE between 1.60 and 2.64 dB(A)), confirming that the resulting models could be employed to forecast noise levels by integrating them into a network of traffic sensors. Full article
(This article belongs to the Special Issue Acoustic Sensing and Monitoring in Urban and Natural Environments)
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12 pages, 457 KiB  
Article
Graph-Based Audio Classification Using Pre-Trained Models and Graph Neural Networks
by Andrés Eduardo Castro-Ospina, Miguel Angel Solarte-Sanchez, Laura Stella Vega-Escobar, Claudia Isaza and Juan David Martínez-Vargas
Sensors 2024, 24(7), 2106; https://doi.org/10.3390/s24072106 - 26 Mar 2024
Viewed by 626
Abstract
Sound classification plays a crucial role in enhancing the interpretation, analysis, and use of acoustic data, leading to a wide range of practical applications, of which environmental sound analysis is one of the most important. In this paper, we explore the representation of [...] Read more.
Sound classification plays a crucial role in enhancing the interpretation, analysis, and use of acoustic data, leading to a wide range of practical applications, of which environmental sound analysis is one of the most important. In this paper, we explore the representation of audio data as graphs in the context of sound classification. We propose a methodology that leverages pre-trained audio models to extract deep features from audio files, which are then employed as node information to build graphs. Subsequently, we train various graph neural networks (GNNs), specifically graph convolutional networks (GCNs), GraphSAGE, and graph attention networks (GATs), to solve multi-class audio classification problems. Our findings underscore the effectiveness of employing graphs to represent audio data. Moreover, they highlight the competitive performance of GNNs in sound classification endeavors, with the GAT model emerging as the top performer, achieving a mean accuracy of 83% in classifying environmental sounds and 91% in identifying the land cover of a site based on its audio recording. In conclusion, this study provides novel insights into the potential of graph representation learning techniques for analyzing audio data. Full article
(This article belongs to the Special Issue Acoustic Sensing and Monitoring in Urban and Natural Environments)
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11 pages, 297 KiB  
Article
Efficient Speech Detection in Environmental Audio Using Acoustic Recognition and Knowledge Distillation
by Drew Priebe, Burooj Ghani and Dan Stowell
Sensors 2024, 24(7), 2046; https://doi.org/10.3390/s24072046 - 22 Mar 2024
Viewed by 775
Abstract
The ongoing biodiversity crisis, driven by factors such as land-use change and global warming, emphasizes the need for effective ecological monitoring methods. Acoustic monitoring of biodiversity has emerged as an important monitoring tool. Detecting human voices in soundscape monitoring projects is useful both [...] Read more.
The ongoing biodiversity crisis, driven by factors such as land-use change and global warming, emphasizes the need for effective ecological monitoring methods. Acoustic monitoring of biodiversity has emerged as an important monitoring tool. Detecting human voices in soundscape monitoring projects is useful both for analyzing human disturbance and for privacy filtering. Despite significant strides in deep learning in recent years, the deployment of large neural networks on compact devices poses challenges due to memory and latency constraints. Our approach focuses on leveraging knowledge distillation techniques to design efficient, lightweight student models for speech detection in bioacoustics. In particular, we employed the MobileNetV3-Small-Pi model to create compact yet effective student architectures to compare against the larger EcoVAD teacher model, a well-regarded voice detection architecture in eco-acoustic monitoring. The comparative analysis included examining various configurations of the MobileNetV3-Small-Pi-derived student models to identify optimal performance. Additionally, a thorough evaluation of different distillation techniques was conducted to ascertain the most effective method for model selection. Our findings revealed that the distilled models exhibited comparable performance to the EcoVAD teacher model, indicating a promising approach to overcoming computational barriers for real-time ecological monitoring. Full article
(This article belongs to the Special Issue Acoustic Sensing and Monitoring in Urban and Natural Environments)
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24 pages, 1221 KiB  
Article
Sons al Balcó: A Comparative Analysis of WASN-Based LAeq Measured Values with Perceptual Questionnaires in Barcelona during the COVID-19 Lockdown
by Daniel Bonet-Solà, Pau Bergadà, Enric Dorca, Carme Martínez-Suquía and Rosa Ma Alsina-Pagès
Sensors 2024, 24(5), 1650; https://doi.org/10.3390/s24051650 - 3 Mar 2024
Viewed by 770
Abstract
The mobility and activity restrictions imposed in Spain due to the COVID-19 pandemic caused a significant improvement in the urban noise pollution that could be objectively measured in those cities with acoustic sensor networks deployed. This significant change in the urban soundscapes was [...] Read more.
The mobility and activity restrictions imposed in Spain due to the COVID-19 pandemic caused a significant improvement in the urban noise pollution that could be objectively measured in those cities with acoustic sensor networks deployed. This significant change in the urban soundscapes was also perceived by citizens who positively appraised this new acoustic scenario. In this work, authors present a comparative analysis between different noise indices provided by 70 sound sensors deployed in Barcelona, both during and before the lockdown, and the results of a perceptual test conducted in the framework of the project Sons al Balcó during the lockdown, which received more than one hundred contributions in Barcelona alone. The analysis has been performed by clustering the objective and subjective data according to the predominant noise sources in the location of the sensors and differentiating road traffic in heavy, moderate and low-traffic areas. The study brings out strong alignments between a decline in noise indices, acoustic satisfaction improvement and changes in the predominant noise sources, supporting the idea that objective calibrated data can be useful to make a qualitative approximation to the subjective perception of urban soundscapes when further information is not available. Full article
(This article belongs to the Special Issue Acoustic Sensing and Monitoring in Urban and Natural Environments)
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24 pages, 79478 KiB  
Article
Blind Calibration of Environmental Acoustics Measurements Using Smartphones
by Ayoub Boumchich, Judicaël Picaut, Pierre Aumond, Arnaud Can and Erwan Bocher
Sensors 2024, 24(4), 1255; https://doi.org/10.3390/s24041255 - 16 Feb 2024
Viewed by 458
Abstract
Environmental noise control is a major health and social issue. Numerous environmental policies require local authorities to draw up noise maps to establish an inventory of the noise environment and then propose action plans to improve its quality. In general, these maps are [...] Read more.
Environmental noise control is a major health and social issue. Numerous environmental policies require local authorities to draw up noise maps to establish an inventory of the noise environment and then propose action plans to improve its quality. In general, these maps are produced using numerical simulations, which may not be sufficiently representative, for example, concerning the temporal dynamics of noise levels. Acoustic sensor measurements are also insufficient in terms of spatial coverage. More recently, an alternative approach has been proposed, consisting of using citizens as data producers by using smartphones as tools of geo-localized acoustic measurement. However, a lack of calibration of smartphones can generate a significant bias in the results obtained. Against the classical metrological principle that would aim to calibrate any sensor beforehand for physical measurement, some have proposed mass calibration procedures called “blind calibration”. The method is based on the crossing of sensors in the same area at the same time, which are therefore supposed to observe the same phenomenon (i.e., measure the same value). The multiple crossings of a large number of sensors at the scale of a territory and the analysis of the relationships between sensors allow for the calibration of the set of sensors. In this article, we propose to adapt a blind calibration method to data from the NoiseCapture smartphone application. The method’s behavior is then tested on NoiseCapture datasets for which information on the calibration values of some smartphones is already available. Full article
(This article belongs to the Special Issue Acoustic Sensing and Monitoring in Urban and Natural Environments)
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11 pages, 2857 KiB  
Article
Passive Acoustic Sampling Enhances Traditional Herpetofauna Sampling Techniques in Urban Environments
by Isabelle L. Barnes and John E. Quinn
Sensors 2023, 23(23), 9322; https://doi.org/10.3390/s23239322 - 22 Nov 2023
Viewed by 1060
Abstract
Data are needed to assess the relationships between urbanization and biodiversity to establish conservation priorities. However, many of these relationships are difficult to fully assess using traditional research methods. To address this gap and evaluate new acoustic sensors and associated data, we conducted [...] Read more.
Data are needed to assess the relationships between urbanization and biodiversity to establish conservation priorities. However, many of these relationships are difficult to fully assess using traditional research methods. To address this gap and evaluate new acoustic sensors and associated data, we conducted a multimethod analysis of biodiversity in a rapidly urbanizing county: Greenville, South Carolina, USA. We conducted audio recordings at 25 points along a development gradient. At the same locations, we used refugia tubes, visual assessments, and an online database. Analysis focused on species identification of both audio and visual data at each point along the trail to determine relationships between both herpetofauna and acoustic indices (as proxies for biodiversity) and environmental gradient of land use and land cover. Our analysis suggests the use of a multitude of different sampling methods to be conducive to the completion of a more comprehensive occupancy measure. Moving forward, this research protocol can potentially be useful in the establishment of more effective wildlife occupancy indices using acoustic sensors to move toward future conservation policies and efforts concerning urbanization, forest fragmentation, and biodiversity in natural, particularly forested, ecosystems. Full article
(This article belongs to the Special Issue Acoustic Sensing and Monitoring in Urban and Natural Environments)
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24 pages, 7450 KiB  
Article
Directional Resonant MEMS Acoustic Sensor and Associated Acoustic Vector Sensor
by Justin Ivancic, Gamani Karunasiri and Fabio Alves
Sensors 2023, 23(19), 8217; https://doi.org/10.3390/s23198217 - 1 Oct 2023
Cited by 2 | Viewed by 1229
Abstract
This paper reports on the design, modeling, analysis, and evaluation of a micro-electromechanical systems acoustic sensor and the novel design of an acoustic vector sensor array (AVS) which utilized this acoustic sensor. This research builds upon previous work conducted to develop a small, [...] Read more.
This paper reports on the design, modeling, analysis, and evaluation of a micro-electromechanical systems acoustic sensor and the novel design of an acoustic vector sensor array (AVS) which utilized this acoustic sensor. This research builds upon previous work conducted to develop a small, lightweight, portable system for the detection and location of quiet or distant acoustic sources of interest. This study also reports on the underwater operation of this sensor and AVS. Studies were conducted in the lab and in the field utilizing multiple acoustic sources (e.g., generated tones, gun shots, drones). The sensor operates at resonance, providing for high acoustic sensitivity and a high signal-to-noise ratio (SNR). The sensor demonstrated a maximum SNR of 88 dB with an associated sensitivity of −84.6 dB re 1 V/μPa (59 V/Pa). The sensor design can be adjusted to set a specified resonant frequency to align with a known acoustic signature of interest. The AVS demonstrated an unambiguous, 360-degree, in-plane, azimuthal coverage and was able to provide an acoustic direction of arrival to an average error of within 3.5° during field experiments. The results of this research demonstrate the potential usefulness of this sensor and AVS design for specific applications. Full article
(This article belongs to the Special Issue Acoustic Sensing and Monitoring in Urban and Natural Environments)
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14 pages, 5513 KiB  
Article
Numerical Analysis of the Mitigation Performance of a Buried PT-WIB on Environmental Vibration
by Lei Gao, Chenzhi Cai, Chao Li and Cheuk Ming Mak
Sensors 2023, 23(18), 7666; https://doi.org/10.3390/s23187666 - 5 Sep 2023
Cited by 2 | Viewed by 813
Abstract
Environmental vibration pollution has serious negative impacts on human health. Among the various contributors to environmental vibration pollution in urban areas, rail transit vibration stands out as a significant source. Consequently, addressing this issue and finding effective measures to attenuate rail transit vibration [...] Read more.
Environmental vibration pollution has serious negative impacts on human health. Among the various contributors to environmental vibration pollution in urban areas, rail transit vibration stands out as a significant source. Consequently, addressing this issue and finding effective measures to attenuate rail transit vibration has become a significant area of concern. An infilled trench can be arranged periodically along the propagation paths of the waves in the soil to attenuate vibration waves in a specific frequency range. However, the periodic infilled trench seems to be unsatisfactory for providing wide band gaps at low and medium frequencies. To improve the isolation performance of wave barriers at low to medium frequencies, a buried PT-WIB consisting of a periodic infilled trench and a wave impedance block barrier has been proposed in this paper. A three-dimensional finite element model has been developed to evaluate the isolation performance of three wave barriers. The influence of the PT-WIB’s parameters on isolation performance has been analyzed. The results indicate that the combined properties of the periodic structure and the wave impedance block barrier can effectively achieve a wide attenuation zone at low and medium frequencies, enhancing the isolation performance for mitigating environmental vibration pollution. Full article
(This article belongs to the Special Issue Acoustic Sensing and Monitoring in Urban and Natural Environments)
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11 pages, 978 KiB  
Article
Hearing to the Unseen: AudioMoth and BirdNET as a Cheap and Easy Method for Monitoring Cryptic Bird Species
by Gerard Bota, Robert Manzano-Rubio, Lidia Catalán, Julia Gómez-Catasús and Cristian Pérez-Granados
Sensors 2023, 23(16), 7176; https://doi.org/10.3390/s23167176 - 15 Aug 2023
Cited by 5 | Viewed by 2847
Abstract
The efficient analyses of sound recordings obtained through passive acoustic monitoring (PAM) might be challenging owing to the vast amount of data collected using such technique. The development of species-specific acoustic recognizers (e.g., through deep learning) may alleviate the time required for sound [...] Read more.
The efficient analyses of sound recordings obtained through passive acoustic monitoring (PAM) might be challenging owing to the vast amount of data collected using such technique. The development of species-specific acoustic recognizers (e.g., through deep learning) may alleviate the time required for sound recordings but are often difficult to create. Here, we evaluate the effectiveness of BirdNET, a new machine learning tool freely available for automated recognition and acoustic data processing, for correctly identifying and detecting two cryptic forest bird species. BirdNET precision was high for both the Coal Tit (Peripatus ater) and the Short-toed Treecreeper (Certhia brachydactyla), with mean values of 92.6% and 87.8%, respectively. Using the default values, BirdNET successfully detected the Coal Tit and the Short-toed Treecreeper in 90.5% and 98.4% of the annotated recordings, respectively. We also tested the impact of variable confidence scores on BirdNET performance and estimated the optimal confidence score for each species. Vocal activity patterns of both species, obtained using PAM and BirdNET, reached their peak during the first two hours after sunrise. We hope that our study may encourage researchers and managers to utilize this user-friendly and ready-to-use software, thus contributing to advancements in acoustic sensing and environmental monitoring. Full article
(This article belongs to the Special Issue Acoustic Sensing and Monitoring in Urban and Natural Environments)
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19 pages, 5595 KiB  
Article
Advanced Noise Indicator Mapping Relying on a City Microphone Network
by Timothy Van Renterghem, Valentin Le Bescond, Luc Dekoninck and Dick Botteldooren
Sensors 2023, 23(13), 5865; https://doi.org/10.3390/s23135865 - 24 Jun 2023
Viewed by 1014
Abstract
In this work, a methodology is presented for city-wide road traffic noise indicator mapping. The need for direct access to traffic data is bypassed by relying on street categorization and a city microphone network. The starting point for the deterministic modeling is a [...] Read more.
In this work, a methodology is presented for city-wide road traffic noise indicator mapping. The need for direct access to traffic data is bypassed by relying on street categorization and a city microphone network. The starting point for the deterministic modeling is a previously developed but simplified dynamic traffic model, the latter necessary to predict statistical and dynamic noise indicators and to estimate the number of noise events. The sound propagation module combines aspects of the CNOSSOS and QSIDE models. In the next step, a machine learning technique—an artificial neural network in this work—is used to weigh the outcomes of the deterministic predictions of various traffic parameter scenarios (linked to street categories) to approach the measured indicators from the microphone network. Application to the city of Barcelona showed that the differences between predictions and measurements typically lie within 2–3 dB, which should be positioned relative to the 3 dB variation in street-side measurements when microphone positioning relative to the façade is not fixed. The number of events is predicted with 30% accuracy. Indicators can be predicted as averages over day, evening and night periods, but also at an hourly scale; shorter time periods do not seem to negatively affect modeling accuracy. The current methodology opens the way to include a broad set of noise indicators in city-wide environmental noise impact assessment. Full article
(This article belongs to the Special Issue Acoustic Sensing and Monitoring in Urban and Natural Environments)
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12 pages, 2105 KiB  
Article
A Quantitative Evaluation of the Performance of the Low-Cost AudioMoth Acoustic Recording Unit
by Sam Lapp, Nickolus Stahlman and Justin Kitzes
Sensors 2023, 23(11), 5254; https://doi.org/10.3390/s23115254 - 1 Jun 2023
Cited by 4 | Viewed by 2117
Abstract
The AudioMoth is a popular autonomous recording unit (ARU) that is widely used to record vocalizing species in the field. Despite its growing use, there have been few quantitative tests on the performance of this recorder. Such information is needed to design effective [...] Read more.
The AudioMoth is a popular autonomous recording unit (ARU) that is widely used to record vocalizing species in the field. Despite its growing use, there have been few quantitative tests on the performance of this recorder. Such information is needed to design effective field surveys and to appropriately analyze recordings made by this device. Here, we report the results of two tests designed to evaluate the performance characteristics of the AudioMoth recorder. First, we performed indoor and outdoor pink noise playback experiments to evaluate how different device settings, orientations, mounting conditions, and housing options affect frequency response patterns. We found little variation in acoustic performance between devices and relatively little effect of placing recorders in a plastic bag for weather protection. The AudioMoth has a mostly flat on-axis response with a boost above 3 kHz, with a generally omnidirectional response that suffers from attenuation behind the recorder, an effect that is accentuated when it is mounted on a tree. Second, we performed battery life tests under a variety of recording frequencies, gain settings, environmental temperatures, and battery types. We found that standard alkaline batteries last for an average of 189 h at room temperature using a 32 kHz sample rate, and that lithium batteries can last for twice as long at freezing temperatures compared to alkaline batteries. This information will aid researchers in both collecting and analyzing recordings generated by the AudioMoth recorder. Full article
(This article belongs to the Special Issue Acoustic Sensing and Monitoring in Urban and Natural Environments)
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22 pages, 15892 KiB  
Article
Toward the Definition of a Soundscape Ranking Index (SRI) in an Urban Park Using Machine Learning Techniques
by Roberto Benocci, Andrea Afify, Andrea Potenza, H. Eduardo Roman and Giovanni Zambon
Sensors 2023, 23(10), 4797; https://doi.org/10.3390/s23104797 - 16 May 2023
Cited by 4 | Viewed by 1880
Abstract
The goal of estimating a soundscape index, aimed at evaluating the contribution of the environmental sound components, is to provide an accurate “acoustic quality” assessment of a complex habitat. Such an index can prove to be a powerful ecological tool associated with both [...] Read more.
The goal of estimating a soundscape index, aimed at evaluating the contribution of the environmental sound components, is to provide an accurate “acoustic quality” assessment of a complex habitat. Such an index can prove to be a powerful ecological tool associated with both rapid on-site and remote surveys. The soundscape ranking index (SRI), introduced by us recently, can empirically account for the contribution of different sound sources by assigning a positive weight to natural sounds (biophony) and a negative weight to anthropogenic ones. The optimization of such weights was performed by training four machine learning algorithms (decision tree, DT; random forest, RF; adaptive boosting, AdaBoost; support vector machine, SVM) over a relatively small fraction of a labeled sound recording dataset. The sound recordings were taken at 16 sites distributed over an area of approximately 22 hectares at Parco Nord (Northern Park) of the city Milan (Italy). From the audio recordings, we extracted four different spectral features: two based on ecoacoustic indices and the other two based on mel-frequency cepstral coefficients (MFCCs). The labeling was focused on the identification of sounds belonging to biophonies and anthropophonies. This preliminary approach revealed that two classification models, DT and AdaBoost, trained by using 84 extracted features from each recording, are able to provide a set of weights characterized by a rather good classification performance (F1-score = 0.70, 0.71). The present results are in quantitative agreement with a self-consistent estimation of the mean SRI values at each site that was recently obtained by us using a different statistical approach. Full article
(This article belongs to the Special Issue Acoustic Sensing and Monitoring in Urban and Natural Environments)
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15 pages, 7789 KiB  
Article
Self-Consistent Soundscape Ranking Index: The Case of an Urban Park
by Roberto Benocci, Andrea Afify, Andrea Potenza, H. Eduardo Roman and Giovanni Zambon
Sensors 2023, 23(7), 3401; https://doi.org/10.3390/s23073401 - 23 Mar 2023
Cited by 3 | Viewed by 1684
Abstract
We have performed a detailed analysis of the soundscape inside an urban park (located in the city of Milan) based on simultaneous sound recordings at 16 locations within the park. The sound sensors were deployed over a regular grid covering an area of [...] Read more.
We have performed a detailed analysis of the soundscape inside an urban park (located in the city of Milan) based on simultaneous sound recordings at 16 locations within the park. The sound sensors were deployed over a regular grid covering an area of about 22 hectares, surrounded by a variety of anthropophonic sources. The recordings span 3.5 h each over a period of four consecutive days. We aimed at determining a soundscape ranking index (SRI) evaluated at each site in the grid by introducing 4 unknown parameters. To this end, a careful aural survey from a single day was performed in order to identify the presence of 19 predefined sound categories within a minute, every 3 minutes of recording. It is found that all SRI values fluctuate considerably within the 70 time intervals considered. The corresponding histograms were used to define a dissimilarity function for each pair of sites. Dissimilarity was found to increase significantly with the inter-site distance in space. Optimal values of the 4 parameters were obtained by minimizing the standard deviation of the data, consistent with a fifth parameter describing the variation of dissimilarity with distance. As a result, we classify the sites into three main categories: “poor”, “medium” and “good” environmental sound quality. This study can be useful to assess the quality of a soundscape in general situations. Full article
(This article belongs to the Special Issue Acoustic Sensing and Monitoring in Urban and Natural Environments)
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