Application of a methodology for categorizing and differentiating urban soundscapes using acoustical descriptors and semantic-differential attributes

Sound source classification for soundscape analysis using fast third-octave bands data from an urban acoustic sensor networka)

The exploration of the soundscape relies strongly on the characterization of the sound sources in the sound environment. Novel sound source classifiers, called pre-trained audio neural networks (PANNs), are capable of predicting the presence of more than 500 diverse sound sources. Nevertheless, PANNs models use fine Mel spectro-temporal representations as input, whereas sensors of an urban noise monitoring network often record fast third-octaves data, which have significantly lower spectro-temporal resolution. In a previous study, we developed a transcoder to transform fast third-octaves into the fine Mel spectro-temporal representation used as input of PANNs. In this paper, we demonstrate that employing PANNs with fast third-octaves data, processed through this transcoder, does not strongly degrade the classifier's performance in predicting the perceived time of presence of sound sources. Through a qualitative analysis of a large-scale fast third-octave dataset, we also illustrate the potential of this tool in opening new perspectives and applications for monitoring the soundscapes of cities.

Acoustic indices as proxies for biodiversity: a meta-analysis

As biodiversity decreases worldwide, the development of effective techniques to track changes in ecological communities becomes an urgent challenge. Together with other emerging methods in ecology, acoustic indices are increasingly being used as novel tools for rapid biodiversity assessment. These indices are based on mathematical formulae that summarise the acoustic features of audio samples, with the aim of extracting meaningful ecological information from soundscapes. However, the application of this automated method has revealed conflicting results across the literature, with conceptual and empirical controversies regarding its primary assumption: a correlation between acoustic and biological diversity. After more than a decade of research, we still lack a statistically informed synthesis of the power of acoustic indices that elucidates whether they effectively function as proxies for biological diversity. Here, we reviewed studies testing the relationship between diversity metrics (species abundance, species richness, species diversity, abundance of sounds, and diversity of sounds) and the 11 most commonly used acoustic indices. From 34 studies, we extracted 364 effect sizes that quantified the magnitude of the direct link between acoustic and biological estimates and conducted a meta-analysis. Overall, acoustic indices had a moderate positive relationship with the diversity metrics (r = 0.33, CI [0.23, 0.43]), and showed an inconsistent performance, with highly variable effect sizes both within and among studies. Over time, studies have been increasingly disregarding the validation of the acoustic estimates and those examining this link have been progressively reporting smaller effect sizes. Some of the studied indices [acoustic entropy index (H), normalised difference soundscape index (NDSI), and acoustic complexity index (ACI)] performed better in retrieving biological information, with abundance of sounds (number of sounds from identified or unidentified species) being the best estimated diversity facet of local communities. We found no effect of the type of monitored environment (terrestrial versus aquatic) and the procedure for extracting biological information (acoustic versus non-acoustic) on the performance of acoustic indices, suggesting certain potential to generalise their application across research contexts. We also identified common statistical issues and knowledge gaps that remain to be addressed in future research, such as a high rate of pseudoreplication and multiple unexplored combinations of metrics, taxa, and regions. Our findings confirm the limitations of acoustic indices to efficiently quantify alpha biodiversity and highlight that caution is necessary when using them as surrogates of diversity metrics, especially if employed as single predictors. Although these tools are able partially to capture changes in diversity metrics, endorsing to some extent the rationale behind acoustic indices and suggesting them as promising bases for future developments, they are far from being direct proxies for biodiversity. To guide more efficient use and future research, we review their principal theoretical and practical shortcomings, as well as prospects and challenges of acoustic indices in biodiversity assessment. Altogether, we provide the first comprehensive and statistically based overview on the relation between acoustic indices and biodiversity and pave the way for a more standardised and informed application for biodiversity monitoring.

Perception of the acoustic environment during COVID-19 lockdown in Argentina

The COVID-19 pandemic has significantly modified the behavior of societies. The application of isolation measures during the crisis resulted in changes in the acoustic environment. The aim of this work was to characterize the perception of the acoustic environment during the COVID-19 lockdown of people residing in Argentina in 2020. A descriptive cross-sectional correlational study was carried out. A virtual survey was conducted from April 14 to 26, 2020, and was answered mainly by social network users. During this period, Argentina was in a strict lockdown. The sample was finally composed of 1371 people between 18 and 79 years old. It was observed that most of the participants preferred the new acoustic environment. Mainly in the larger cities, before the isolation, mechanical sounds predominated, accompanied by the perception of irritation. Confinement brought a decrease in mechanical sounds and an increase in biological sounds, associated with feelings of tranquility and happiness. The time window opened by the lockdown offered an interesting scenario to assess the effect of anthropogenic noise pollution on the urban environment. This result offers a subjective approach, which contributes to understanding the link between individuals and communities with the environment.

A Taxonomy Proposal for the Assessment of the Changes in Soundscape Resulting from the COVID-19 Lockdown

Many countries around the world have chosen lockdown and restrictions on people's mobility as the main strategies to combat the COVID-19 pandemic. These actions have significantly affected environmental noise and modified urban soundscapes, opening up an unprecedented opportunity for research in the field. In order to enable these investigations to be carried out in a more harmonized and consistent manner, this paper makes a proposal for a set of indicators that will enable to address the challenge from a number of different approaches. It proposes a minimum set of basic energetic indicators, and the taxonomy that will allow their communication and reporting. In addition, an extended set of descriptors is outlined which better enables the application of more novel approaches to the evaluation of the effect of this new soundscape on people's subjective perception.

A Taxonomy Proposal for the Assessment of the Changes in Soundscape Resulting from the COVID-19 Lockdown

Many countries around the world have chosen lockdown and restrictions on people's mobility as the main strategies to combat the COVID-19 pandemic. These actions have significantly affected environmental noise and modified urban soundscapes, opening up an unprecedented opportunity for research in the field. In order to enable these investigations to be carried out in a more harmonized and consistent manner, this paper makes a proposal for a set of indicators that will enable to address the challenge from a number of different approaches. It proposes a minimum set of basic energetic indicators, and the taxonomy that will allow their communication and reporting. In addition, an extended set of descriptors is outlined which better enables the application of more novel approaches to the evaluation of the effect of this new soundscape on people's subjective perception.

Acoustic indices as rapid indicators of avian diversity in different land-use types in an Indian biodiversity hotspot

Passive acoustic monitoring is a potentially valuable tool in biodiversity hotspots, where surveying can occur at large scales across land conversion types. However, in order to extract meaningful biological information from resulting enormous acoustic datasets, rapid analytical techniques are required. Here we tested the ability of a suite of acoustic indices to predict avian bioacoustic activity in recordings collected from the Western Ghats, a biodiversity hotspot in southwestern India. Recordings were collected at 28 sites in a range of land-use types, from tea, coffee, and cardamom plantations to remnant forest stands. Using 36 acoustic indices we developed random forest models to predict the richness, diversity, and total number of avian vocalizations observed in recordings. We found limited evidence that acoustic indices predict the richness and total number of avian species vocalizations in recordings (R2 < 0.51). However, acoustic indices predicted the diversity of avian species vocalizations with high accuracy (R2 = 0.64, mean squared error = 0.17). Index models predicted low and high diversity best, with the highest residuals for medium diversity values and when continuous biological sounds were present (e.g., insect sounds >8 sec). The acoustic complexity index and roughness index were the most important for predicting avian vocal diversity. Avian species richness was generally higher among shade-grown crops than in the open tea plantation. Our results suggest that models incorporating acoustic indices can accurately predict low and high avian species diversity from acoustic recordings. Thus, ecoacoustics could be an important contributor to biodiversity monitoring across landscapes like the Western Ghats, which are a complex mosaic of different land-use types and face continued changes in the future.

An Efficient Audio Coding Scheme for Quantitative and Qualitative Large Scale Acoustic Monitoring Using the Sensor Grid Approach

The spreading of urban areas and the growth of human population worldwide raise societal and environmental concerns. To better address these concerns, the monitoring of the acoustic environment in urban as well as rural or wilderness areas is an important matter. Building on the recent development of low cost hardware acoustic sensors, we propose in this paper to consider a sensor grid approach to tackle this issue. In this kind of approach, the crucial question is the nature of the data that are transmitted from the sensors to the processing and archival servers. To this end, we propose an efficient audio coding scheme based on third octave band spectral representation that allows: (1) the estimation of standard acoustic indicators; and (2) the recognition of acoustic events at state-of-the-art performance rate. The former is useful to provide quantitative information about the acoustic environment, while the latter is useful to gather qualitative information and build perceptually motivated indicators using for example the emergence of a given sound source. The coding scheme is also demonstrated to transmit spectrally encoded data that, reverted to the time domain using state-of-the-art techniques, are not intelligible, thus protecting the privacy of citizens.

Perceptions and effects of the acoustic environment in quiet residential areas

Many cities have historical areas clearly distinguished from the rest because of the architecture, urban planning, and functionality. In many cases, these aspects give one the possibility of finding a characteristic acoustic environment and also developing quiet areas. Through an examination of sound levels and surveys, the perception of residents and passers-by concerning the acoustic environment of the old town of Cáceres and its relation with the characteristics of the urban environment were analysed. In addition, the perception and the effects of noise pollution of low intensity were studied. The results indicate that absence of daytime noise is the most influential environmental characteristic on the overall perception of the urban environment studied, even surpassing the feeling of security. The absence of daytime noise was also the most valued characteristic of the urban environment according to respondents. The most annoying noise source proved to be the road traffic. However, for similar levels of sound exposure, the percentages of people who were annoyed and whose sleep was disturbed were lower than those found in previous studies. Bells and birds, both soundmarks of the soundscape of this urban environment, were among the most annoying to passers-by.

Do Biology Students Really Hate Math? Empirical Insights into Undergraduate Life Science Majors' Emotions about Mathematics

Undergraduate life science majors are reputed to have negative emotions toward mathematics, yet little empirical evidence supports this. We sought to compare emotions of majors in the life sciences versus other natural sciences and math. We adapted the Attitudes toward the Subject of Chemistry Inventory to create an Attitudes toward the Subject of Mathematics Inventory (ASMI). We collected data from 359 science and math majors at two research universities and conducted a series of statistical tests that indicated that four AMSI items comprised a reasonable measure of students' emotional satisfaction with math. We then compared life science and non-life science majors and found that major had a small to moderate relationship with students' responses. Gender also had a small relationship with students' responses, while students' race, ethnicity, and year in school had no observable relationship. Using latent profile analysis, we identified three groups-students who were emotionally satisfied with math, emotionally dissatisfied with math, and neutral. These results and the emotional satisfaction with math scale should be useful for identifying differences in other undergraduate populations, determining the malleability of undergraduates' emotional satisfaction with math, and testing effects of interventions aimed at improving life science majors' attitudes toward math.

Describing and classifying urban sound environments with a relevant set of physical indicators

Categorization is a powerful method for describing urban sound environments. However, it has only been applied, until now, to discrete noise data collection, whereas sound environments vary continuously both in space and time. Therefore, a procedure is developed in this paper for describing the variations of urban sound environments. The procedure consists of mobile measurements, followed by a statistical clustering analysis that selects relevant noise indicators and classifies sound environments. Analysis are based on a 3 days + 1 night survey where geo-referenced noise measurements were collected over 19 1-h soundwalk periods in a district of Marseille, France. The clustering analysis showed that a limited subset of indicators is sufficient to discriminate sound environments. The three indicators that emerged from the clustering, namely, the Leq, A, the standard deviation σL eq, A, and the sound gravity spectrum SGC[50 Hz-10 kHz], are consistent with previous studies on sound environment classification. Moreover, the procedure proposed enables the description of the sound environment, which is classified into homogenous sound environment classes by means of the selected indicators. Thus, the procedure can be adapted to any urban environment, and can, for instance, favorably enhance perceptive studies by delimiting precisely the spatial extent of each typical sound environment.

A tool for urban soundscape evaluation applying Support Vector Machines for developing a soundscape classification model

To ensure appropriate soundscape management in urban environments, the urban-planning authorities need a range of tools that enable such a task to be performed. An essential step during the management of urban areas from a sound standpoint should be the evaluation of the soundscape in such an area. In this sense, it has been widely acknowledged that a subjective and acoustical categorization of a soundscape is the first step to evaluate it, providing a basis for designing or adapting it to match people's expectations as well. In this sense, this work proposes a model for automatic classification of urban soundscapes. This model is intended for the automatic classification of urban soundscapes based on underlying acoustical and perceptual criteria. Thus, this classification model is proposed to be used as a tool for a comprehensive urban soundscape evaluation. Because of the great complexity associated with the problem, two machine learning techniques, Support Vector Machines (SVM) and Support Vector Machines trained with Sequential Minimal Optimization (SMO), are implemented in developing model classification. The results indicate that the SMO model outperforms the SVM model in the specific task of soundscape classification. With the implementation of the SMO algorithm, the classification model achieves an outstanding performance (91.3% of instances correctly classified).