Cartographic Representation of the Sonic Environment

Papadimitriou, Kimon; Mazaris, Antonios D.; Kallimanis, Athanasios S.; Pantis, John D.

doi:10.1179/000870409x459842

Cited by 26 publications

(13 citation statements)

References 18 publications

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“…As [31] mention, one possible reason is the absence of objective data to generate such maps compared to noise maps. Previous works in this field refer to the spatial representation of loudness and soundscape quality [26,32], or the soundscape ecology in parks [27] and rural areas [38]. As expected, the majority of these studies are disconnected from the planning process or present the potential to be integrated in this field.…”

Section: Model Development For Soundscape Mapsmentioning

confidence: 99%

“…In terms of mapping content, for parks or rural areas, a suitable categorization of sound sources can follow the example of [38] or [27] which is nature-oriented (anthropophony, biophony, geophony). Nonetheless, for ur- ban environments a categorization that can be more representative is closer to the taxonomy of human, natural and technological sources previously used in other studies as well [32,41,42].…”

Section: Model Effectivenessmentioning

confidence: 99%

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Soundscape mapping in environmental noise management and urban planning: case studies in two UK cities

Margaritis

Kang

2017

Noise Mapping

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Section: Model Development For Soundscape Mapsmentioning

confidence: 99%

Section: Model Effectivenessmentioning

confidence: 99%

Soundscape mapping in environmental noise management and urban planning: case studies in two UK cities

Margaritis

Kang

2017

Noise Mapping

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“…The first level of identification hierarchy consists in identifying the general sound type, for example, whether it originates from human (anthropogenic) or animal (biogenerated other than human) activity or whether it is an ambient natural sound (e.g., waterfall and fire). This categorization is very popular in the respective literature [26][27][28]. The second level of identification hierarchy consists in further identifying for each sound type a more focused sound origin; for example, whether an animal activity is actually a bird, a snake, or, say, a dog.…”

Section: Sound Classification Approachesmentioning

confidence: 99%

A Two-Level Sound Classification Platform for Environmental Monitoring

et al. 2018

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STORM is an ongoing European research project that aims at developing an integrated platform for monitoring, protecting, and managing cultural heritage sites through technical and organizational innovation. Part of the scheduled preventive actions for the protection of cultural heritage is the development of wireless acoustic sensor networks (WASNs) that will be used for assessing the impact of human-generated activities as well as for monitoring potentially hazardous environmental phenomena. Collected sound samples will be forwarded to a central server where they will be automatically classified in a hierarchical manner; anthropogenic and environmental activity will be monitored, and stakeholders will be alarmed in the case of potential malevolent behavior or natural phenomena like excess rainfall, fire, gale, high tides, and waves. Herein, we present an integrated platform that includes sound sample denoising using wavelets, feature extraction from sound samples, Gaussian mixture modeling of these features, and a powerful two-layer neural network for automatic classification. We contribute to previous work by extending the proposed classification platform to perform low-level classification too, i.e., classify sounds to further subclasses that include airplane, car, and pistol sounds for the anthropogenic sound class; bird, dog, and snake sounds for the biophysical sound class; and fire, waterfall, and gale for the geophysical sound class. Classification results exhibit outstanding classification accuracy in both high-level and low-level classification thus demonstrating the feasibility of the proposed approach.

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“…It is important to note, however, that discussions concerning noise pollution imply and perhaps overemphasise the negative aspects of the sound environment (Papadimitriou et al, 2009). We are all aware and have experiences of sounds that are not only associated with negative feelings and emotions but also with positive feelings and emotions, e.g., birds, music, etc.…”

Section: Noise Mapping and Environmental Sustainabilitymentioning

confidence: 99%