Road traffic sound level estimation from realistic urban sound mixtures by Non-negative Matrix Factorization

Gloaguen, Jean-Rémy; Can, Arnaud; Lagrange, Mathieu; Petiot, Jean-François

doi:10.1016/j.apacoust.2018.08.018

Cited by 19 publications

(12 citation statements)

References 44 publications

(28 reference statements)

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“…Therefore, the development of source-orientated indicators, able to quantify the presence of sources of interest, and ideally performing with urban sound mixtures with strong temporal overlaps, is strongly advocated. Premises towards such indicators can be found in the literature, relying on sound recognition [25,[47][48][49].…”

Section: Discussionmentioning

confidence: 99%

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

Asensio

Aumond

Can

et al. 2020

IJERPH

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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.

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Section: Discussionmentioning

confidence: 99%

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

Asensio

Aumond

Can

et al. 2020

IJERPH

Self Cite

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“…In [76], a AED approach based on nonnegative matrix factorization (NMF) and shortterm fast Fourier transform (FFT) is introduced with the aim of isolating the contribution of road traffic noise from the measurements of urban sound mixtures. is work has been recently applied to estimate road traffic sound levels [77], showing good results within a synthetically generated database following a similar approach as the one described in [78].…”

Section: Acoustic Event Detection In Urbanmentioning

confidence: 99%

Review of Wireless Acoustic Sensor Networks for Environmental Noise Monitoring in Smart Cities

Álías

Alsina-Pagès

2019

Journal of Sensors

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Nowadays, more than half of the world’s population lives in urban areas. Since this proportion is expected to keep rising, the sustainable development of cities is of paramount importance to guarantee the quality of life of their inhabitants. Environmental noise is one of the main concerns that has to be addressed, due to its negative impact on the health of people. Different national and international noise directives and legislations have been defined during the past decades, which local authorities must comply with involving noise mapping, action plans, policing, and public awareness, among others. To this aim, a recent change in the paradigm for environmental noise monitoring has been driven by the rise of Internet of Things technology within smart cities through the design and development of wireless acoustic sensor networks (WASNs). This work reviews the most relevant WASN-based approaches developed to date focused on environmental noise monitoring. The proposals have moved from networks composed of high-accuracy commercial devices to the those integrated by ad hoc low-cost acoustic sensors, sometimes designed as hybrid networks with low and high computational capacity nodes. After describing the main characteristics of recent WASN-based projects, the paper also discusses several open challenges, such as the development of acoustic signal processing techniques to identify noise events, to allow the reliable and pervasive deployment of WASNs in urban areas together with some potential future applications.

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“…Even more, Non-Negative Matrix Factorization (NMF) is a hot topic in machine learning and it has been successfully applied to various tasks, such as structure from-motion problems, acoustic event classification, and recommender systems [25,30]. NMF is outstanding from many algorithms due to the characteristics of variable dimensions, which means that an observed matrix can be approximately decomposed into two low rank matrices represented positive basis vectors and coefficients weighting [26,27]. Inspired by the special properties, the limited time series of monitoring data could be used to deduce a multi-dimensional matrix representing the mechanical variation of more spatial positions.…”

Section: Introductionmentioning

confidence: 99%

Analysis for full face mechanical behaviors through spatial deduction model with real-time monitoring data

Tan¹,

Wang²,

Du³

et al. 2021

Preprint

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Mechanical analysis for the full face of tunnel structure is crucial to maintain stability, which is a challenge in classical analytical solutions and data analysis. Along this line, this study aims to develop a spatial deduction model to obtain the full-faced mechanical behaviors through integrating mechanical properties into pure data-driven model. The spatial tunnel structure is divided into many parts and reconstructed in a form of matrix. Then, the external load applied on structure in the field was considered to study the mechanical behaviors of tunnel. Based on the limited observed monitoring data in matrix and mechanical analysis results, a double-driven model was developed to obtain the full-faced information, in which the data-driven model was the dominant one and the mechanical constraint was the secondary one. To verify the presented spatial deduction model, cross-test was conducted through assuming partial monitoring data are unknown and regarding them as testing points. The well agreement between deduction results with actual monitoring results means the proposed model is reasonable. Therefore, it was employed to deduct both the current and historical performance of tunnel full face, which is crucial to prevent structural disasters.

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Road traffic sound level estimation from realistic urban sound mixtures by Non-negative Matrix Factorization

Cited by 19 publications

References 44 publications

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

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

Review of Wireless Acoustic Sensor Networks for Environmental Noise Monitoring in Smart Cities

Analysis for full face mechanical behaviors through spatial deduction model with real-time monitoring data

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