scholarly journals Estimation of the Perceived Time of Presence of Sources in Urban Acoustic Environments Using Deep Learning Techniques

2019 ◽  
Vol 105 (6) ◽  
pp. 1053-1066 ◽  
Author(s):  
Félix Gontier ◽  
Catherine Lavandier ◽  
Pierre Aumond ◽  
Mathieu Lagrange ◽  
Jean-François Petiot
Keyword(s):  
Positive Impact ◽  
Noise Pollution ◽  
Point Of View ◽  
Machine Learning Techniques ◽  
Human Beings ◽  
Sound Sources ◽  
Detection Model ◽  
Learning Techniques ◽  
Acoustic Environments ◽  
The Impact

The impact of urban sound on human beings has often been studied from a negative point of view (noise pollution). In the two last decades, the interest of studying its positive impact has been revealed with the soundscape approach (resourcing spaces). The literature shows that the recognition of sources plays a great role in the way humans are affected by sound environments. There is thus a need for characterizing urban acoustic environments not only with sound pressure measurements but also with source-specific attributes such as their perceived time of presence, dominance or volume. This paper demonstrates, on a controlled dataset, that machine learning techniques based on state of the art neural architectures can predict the perceived time of presence of several sound sources at a sufficient accuracy. To validate this assertion, a corpus of simulated sound scenes is first designed. Perceptual attributes corresponding to those stimuli are gathered through a listening experiment. From the contributions of the individual sound sources available for the simulated corpus, a physical indicator approximating the perceived time of presence of sources is computed and used to train and evaluate a multi-label source detection model. This model predicts the presence of simultaneously active sources from fast third octave spectra, allowing the estimation of perceptual attributes such as pleasantness in urban sound environments at a sufficient degree of precision.

scholarly journals The Impact of Innovation Strategy on the Financial, Social and Environmental Performance

2021 ◽  
Vol 1 (1) ◽  
pp. 33-39
Author(s):  
Hamid Saremi ◽  
Masoud Mahmoudi ◽  
Mojtaba Soltaninezhad ◽  
Mohammad Hosseinpour
Keyword(s):  
Financial Performance ◽  
Environmental Performance ◽  
Positive Impact ◽  
Stock Exchange ◽  
Social Performance ◽  
Point Of View ◽  
Innovation Strategy ◽  
Management Tools ◽  
Social And Environmental Performance ◽  
The Impact

The core purpose of this study is to investigate the effect of innovation strategy on financial, social and environmental performance of companies listed on the Tehran Stock Exchange (TSE). The information used is from 129 companies listed on TSE in different industries between 2011 and 2018 (1032 observations). In order to analyze the data, a multivariate regression test was used. The results showed a positive and significant relationship between innovation strategy on financial performance and environmental performance. Also, the relationship between innovation strategy and social performance has a positive but insignificant. Innovation tools are also among the few management tools that can have a positive impact on both financial performance and the company's environmental performance. In this research, an attempt has been made to look at the idea of innovation from a financial point of view, and its results in the long run indicate the right choice of management to invest in the company's research and development unit.

scholarly journals A Heuristic Model for Predicting Human Fall Detection using Machine Learning Techniques

2020 ◽  
Vol 9 (2) ◽  
pp. 119-121
Keyword(s):  
Machine Learning ◽  
Health Problem ◽  
Fall Detection ◽  
Machine Learning Techniques ◽  
Technological Advancement ◽  
Heuristic Model ◽  
Human Beings ◽  
Machine Learning Classifiers ◽  
Learning Techniques ◽  
Smart Health

It is very obvious that human fall due to unconsciousness is a very common health problem in every human being. With the evolution of many smart health devices, we should contribute the technological advancement of machine learning into it. Different techniques are already used in order to detect human fall detection in human beings. In this paper we have studied the patterns of falling of human through the fall detection dataset while this human was performing various motions. By understanding all these we have generated the prediction protocol which estimates the fall of a person using fall detection dataset. Machine Learning classifiers were used to predict the human fall and a comparative study of various algorithms used was developed to find out the best classifier.

scholarly journals Machine learning analysis of lifeguard flag decisions and recorded rescues

2019 ◽  
Vol 19 (11) ◽  
pp. 2541-2549
Author(s):  
Chris Houser ◽  
Jacob Lehner ◽  
Nathan Cherry ◽  
Phil Wernette
Keyword(s):  
Machine Learning ◽  
The United States ◽  
Machine Learning Techniques ◽  
Public Health Issue ◽  
Rip Currents ◽  
Effective Strategies ◽  
Wave Forcing ◽  
Learning Techniques ◽  
Yellow Flag ◽  
The Impact

Abstract. Rip currents and other surf hazards are an emerging public health issue globally. Lifeguards, warning flags, and signs are important, and to varying degrees they are effective strategies to minimize risk to beach users. In the United States and other jurisdictions around the world, lifeguards use coloured flags (green, yellow, and red) to indicate whether the danger posed by the surf and rip hazard is low, moderate, or high respectively. The choice of flag depends on the lifeguard(s) monitoring the changing surf conditions along the beach and over the course of the day using both regional surf forecasts and careful observation. There is a potential that the chosen flag is not consistent with the beach user perception of the risk, which may increase the potential for rescues or drownings. In this study, machine learning is used to determine the potential for error in the flags used at Pensacola Beach and the impact of that error on the number of rescues. Results of a decision tree analysis indicate that the colour flag chosen by the lifeguards was different from what the model predicted for 35 % of days between 2004 and 2008 (n=396/1125). Days when there is a difference between the predicted and posted flag colour represent only 17 % of all rescue days, but those days are associated with ∼60 % of all rescues between 2004 and 2008. Further analysis reveals that the largest number of rescue days and total number of rescues are associated with days where the flag deployed over-estimated the surf and hazard risk, such as a red or yellow flag flying when the model predicted a green flag would be more appropriate based on the wind and wave forcing alone. While it is possible that the lifeguards were overly cautious, it is argued that they most likely identified a rip forced by a transverse-bar and rip morphology common at the study site. Regardless, the results suggest that beach users may be discounting lifeguard warnings if the flag colour is not consistent with how they perceive the surf hazard or the regional forecast. Results suggest that machine learning techniques have the potential to support lifeguards and thereby reduce the number of rescues and drownings.

scholarly journals Monitoring the Impact of Air Quality on the COVID-19 Fatalities in Delhi, India: Using Machine Learning Techniques

2020 ◽  
pp. 1-8
Author(s):  
Jasleen Kaur Sethi ◽  
Mamta Mittal
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Air Pollutants ◽  
Machine Learning Techniques ◽  
Environmental Restoration ◽  
The Novel ◽  
Ozone Pollution ◽  
Learning Techniques ◽  
Novel Coronavirus ◽  
The Impact

ABSTRACT Objective: The focus of this study is to monitor the effect of lockdown on the various air pollutants due to the coronavirus disease (COVID-19) pandemic and identify the ones that affect COVID-19 fatalities so that measures to control the pollution could be enforced. Methods: Various machine learning techniques: Decision Trees, Linear Regression, and Random Forest have been applied to correlate air pollutants and COVID-19 fatalities in Delhi. Furthermore, a comparison between the concentration of various air pollutants and the air quality index during the lockdown period and last two years, 2018 and 2019, has been presented. Results: From the experimental work, it has been observed that the pollutants ozone and toluene have increased during the lockdown period. It has also been deduced that the pollutants that may impact the mortalities due to COVID-19 are ozone, NH3, NO2, and PM10. Conclusions: The novel coronavirus has led to environmental restoration due to lockdown. However, there is a need to impose measures to control ozone pollution, as there has been a significant increase in its concentration and it also impacts the COVID-19 mortality rate.

scholarly journals Knocking and Listening: Learning Mechanical Impulse Response for Understanding Surface Characteristics

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 369 ◽  
Author(s):  
Semin Ryu ◽  
Seung-Chan Kim
Keyword(s):  
Intelligent System ◽  
Surface Characteristics ◽  
Machine Learning Techniques ◽  
Test Accuracy ◽  
Learning Approaches ◽  
Well Test ◽  
Mechanical Responses ◽  
Learning Techniques ◽  
The Impact ◽  
Mechanical Impulse

Inspired by spiders that can generate and sense vibrations to obtain information regarding a substrate, we propose an intelligent system that can recognize the type of surface being touched by knocking the surface and listening to the vibrations. Hence, we developed a system that is equipped with an electromagnetic hammer for hitting the ground and an accelerometer for measuring the mechanical responses induced by the impact. We investigate the feasibility of sensing 10 different daily surfaces through various machine-learning techniques including recent deep-learning approaches. Although some test surfaces are similar, experimental results show that our system can recognize 10 different surfaces remarkably well (test accuracy of 98.66%). In addition, our results without directly hitting the surface (internal impact) exhibited considerably high test accuracy (97.51%). Finally, we conclude this paper with the limitations and future directions of the study.

What Nonresident Mothers and Fathers Have to Say About a Mother-Only Coparenting Intervention: A Qualitative Assessment of Understanding DadsTM

2020 ◽  
Vol 101 (2) ◽  
pp. 167-179
Author(s):  
Jessica Pearson ◽  
Abigail Henson ◽  
Jay Fagan
Keyword(s):  
Father Involvement ◽  
Positive Impact ◽  
Behavioral Outcomes ◽  
Point Of View ◽  
Qualitative Assessment ◽  
Nonresident Fathers ◽  
Mothers And Fathers ◽  
Nonresident Parents ◽  
The Impact

Coparenting between mothers and nonresident fathers is a consistent predictor of positive father involvement and is shown to have a direct positive impact on children’s behavioral outcomes. While many fatherhood programs attempt to improve coparenting relationships using father-only interventions, the information on their effectiveness is mixed. Couple interventions may be more effective than father-only approaches but are very hard to achieve with nonresident parents. Engaging mothers may be more practical and beneficial, although there is very little literature on the impact of mother-only interventions on coparenting relationships. The current study begins to address that gap. It presents qualitative reactions by mothers and fathers to a mother-only coparenting intervention and finds that a mother-only approach can achieve some important goals such as improved communication, reduced conflict, and mother’s understanding of the father’s point of view. Fathers whose parenting partners participated in the mother-only group agreed with mothers’ assessments and also reported less undermining.

scholarly journals ZARATAMAP: Noise Characterization in the Scope of a Smart City through a Low Cost and Mobile Electronic Embedded System

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1707
Author(s):  
Unai Hernandez-Jayo ◽  
Amaia Goñi
Keyword(s):  
Embedded System ◽  
Low Cost ◽  
Noise Pollution ◽  
Mobile Node ◽  
Machine Learning Techniques ◽  
Potential Harm ◽  
Sources Of Pollution ◽  
Learning Techniques ◽  
Noise Characterization

Like other sources of pollution, noise is considered to be one of the main concerns of citizens, due to its invisibility and the potential harm it can cause. Noise pollution could be considered as one of the biggest quality-of-life concerns for urban residents in big cities, mainly due to the high levels of noise to which they may be exposed. Such levels have proven effects on health, such as: sleep disruption, hypertension, heart disease, and hearing loss. In a scenario where the number of people concentrated in cities is increasing, tools are needed to quantify, monitor, characterize, and quantify noise levels. This paper presents the ZARATAMAP project, which combines machine learning techniques with a geo-sensing application so that the authorities can have as much information as possible, using a low-cost embedded and mobile node, that is easy to deploy, develop, and use.

From machine learning to sustainable taxation: GPS traces of trucks circulating in Belgium

2020 ◽  
Author(s):  
Arnaud Adam ◽  
Isabelle Thomas
Keyword(s):  
Machine Learning ◽  
Point Of View ◽  
Machine Learning Techniques ◽  
Spatial Effects ◽  
Space And Time ◽  
Learning Techniques ◽  
Other Information ◽  
Gps Traces ◽  
The Individual ◽  
Taxation System

<p>Transport geography has always been characterized by a lack of accurate data, leading to surveys often based on samples that are spatially not representative. However, the current deluge of data collected through sensors promises to overpass this scarcity of data. We here consider one example: since April 1<sup>st</sup> 2016, a GPS tracker is mandatory within each truck circulating in Belgium for kilometre taxes. Every 30 seconds, this tracker collects the position of the truck (as well as some other information such as speed or direction), leading to an individual taxation of trucks. This contribution uses a one-week exhaustive database containing the totality of trucks circulating in Belgium, in order to understand transport fluxes within the country, as well as the spatial effects of the taxation on the circulation of trucks.</p><p>Machine learning techniques are applied on over 270 million of GPS points to detect stops of trucks, leading to transform GPS sequences into a complete Origin-Destination matrix. Using machine learning allows to accurately classify stops that are different in nature (leisure stop, (un-)loading areas, or congested roads). Based on this matrix, we firstly propose an overview of the daily traffic, as well as an evaluation of the number of stops made in every Belgian place. Secondly, GPS sequences and stops are combined, leading to characterise sub-trajectories of each truck (first/last miles and transit) by their fiscal debit. This individual characterisation, as well as its variation in space and time, are here discussed: is the individual taxation system always efficient in space and time?</p><p>This contribution helps to better understand the circulation of trucks in Belgium, the places where they stopped, as well as the importance of their locations in a fiscal point of view. What are the potential modifications of the trucks routes that would lead to a more sustainable kilometre taxation? This contribution illustrates that combining big-data and machine learning open new roads for accurately measuring and modelling transportation.</p>

Convolutional Recurrent Neural Networks Based Speech Emotion Recognition

2020 ◽  
Vol 17 (8) ◽  
pp. 3786-3789
Author(s):  
P. Gayathri ◽  
P. Gowri Priya ◽  
L. Sravani ◽  
Sandra Johnson ◽  
Visanth Sampath
Keyword(s):  
Neural Networks ◽  
Emotion Recognition ◽  
Recurrent Neural Networks ◽  
Machine Learning Techniques ◽  
Speech Emotion Recognition ◽  
Emotional Information ◽  
Feature Representations ◽  
Emotional Factors ◽  
Learning Techniques ◽  
The Impact

Recognition of emotions is the aspect of speech recognition that is gaining more attention and the need for it is growing enormously. Although there are methods to identify emotion using machine learning techniques, we assume in this paper that calculating deltas and delta-deltas for customized features not only preserves effective emotional information, but also that the impact of irrelevant emotional factors, leading to a reduction in misclassification. Furthermore, Speech Emotion Recognition (SER) often suffers from the silent frames and irrelevant emotional frames. Meanwhile, the process of attention has demonstrated exceptional performance in learning related feature representations for specific tasks. Inspired by this, propose a Convolutionary Recurrent Neural Networks (ACRNN) based on Attention to learn discriminative features for SER, where the Mel-spectrogram with deltas and delta-deltas is used as input. Finally, experimental results show the feasibility of the proposed method and attain state-of-the-art performance in terms of unweighted average recall.

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